See: https://github.com/PrototypeJam/agento_app/issues/17

See below for: 1) Example code with traces I built myself (module1-opentelemetry-gm-1130.py) followed by 2) example of exported traces from that code (manual_traces.json).But first, an idea for how to apply this home-built traces system to the current Agento Streamlit app and to include API calls to Gemini and Claude with custom traces as well!

I can never get the OpenAI Traces to Work so I Built My Own

Approach to Using My Manual Traces with Other APIs for Claude and Gemini

Yes, you can absolutely use your manual tracing approach with other major LLM API providers like Claude and Gemini! Your manual tracing system is completely independent of any specific LLM provider's SDK, which makes it highly portable.

Why Your Manual System Works Everywhere

Your manual tracing system has these key characteristics:

1. It's provider-agnostic - It just captures stage names, inputs, outputs, and timestamps
2. It's SDK-independent - It doesn't rely on any OpenAI-specific features
3. It's explicit - You control exactly what gets captured and when
4. It's simple - Just JSON data structures saved to files

How to Use It with Claude (Anthropic)

Based on my search, Claude uses the Anthropic SDK with syntax like from anthropic import Anthropic and client = Anthropic(). Here's how to adapt your system:

import anthropic
from anthropic import Anthropic

# Your existing manual trace system (no changes needed!)
MANUAL_TRACES = {
    "traces": [],
    "current_trace": None
}

def capture_step(stage, inputs, outputs, trace_id=None):
    # Your existing implementation - NO CHANGES NEEDED
    pass

# Using with Claude
async def run_with_claude(user_goal: str):
    process_id = f"process_{uuid.uuid4().hex}"
    
    # Capture process start
    capture_step("process_start", {"timestamp": datetime.datetime.now().isoformat()}, None, process_id)
    
    # Initialize Claude client
    client = Anthropic()  # Uses ANTHROPIC_API_KEY env var
    
    # Capture before Claude call
    capture_step("claude_request_start", {"model": "claude-3-5-sonnet-latest", "goal": user_goal}, None, process_id)
    
    # Make Claude API call
    message = client.messages.create(
        model="claude-3-5-sonnet-latest",
        max_tokens=1024,
        messages=[{"role": "user", "content": f"Generate success criteria for: {user_goal}"}]
    )
    
    # Capture Claude response
    capture_step("claude_response", 
                {"model": "claude-3-5-sonnet-latest"}, 
                {"response": message.content[0].text}, 
                process_id)
    
    # Continue with your workflow...

How to Use It with Gemini (Google)

Based on my search, Google's new unified SDK uses from google import genai and supports both Gemini Developer API and Vertex AI:

from google import genai

# Your manual trace system remains exactly the same!

async def run_with_gemini(user_goal: str):
    process_id = f"process_{uuid.uuid4().hex}"
    
    # Capture process start
    capture_step("process_start", {"timestamp": datetime.datetime.now().isoformat()}, None, process_id)
    
    # Initialize Gemini client
    client = genai.Client(api_key='GEMINI_API_KEY')  # Or use Vertex AI
    
    # Capture before Gemini call
    capture_step("gemini_request_start", {"model": "gemini-2.0-flash-001", "goal": user_goal}, None, process_id)
    
    # Make Gemini API call
    response = client.models.generate_content(
        model='gemini-2.0-flash-001',
        contents=f"Generate success criteria for: {user_goal}"
    )
    
    # Capture Gemini response
    capture_step("gemini_response", 
                {"model": "gemini-2.0-flash-001"}, 
                {"response": response.text}, 
                process_id)
    
    # Continue with your workflow...

Creating a Provider-Agnostic Wrapper

You can even create a unified interface that works with all providers:

class UnifiedLLMClient:
    def __init__(self, provider="openai", process_id=None):
        self.provider = provider
        self.process_id = process_id or f"process_{uuid.uuid4().hex}"
        
        if provider == "openai":
            from openai import OpenAI
            self.client = OpenAI()
        elif provider == "anthropic":
            from anthropic import Anthropic
            self.client = Anthropic()
        elif provider == "gemini":
            from google import genai
            self.client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
    
    async def generate(self, prompt, model=None):
        # Capture request
        capture_step(f"{self.provider}_request", 
                    {"prompt": prompt, "model": model}, 
                    None, 
                    self.process_id)
        
        # Provider-specific generation
        if self.provider == "openai":
            response = self.client.chat.completions.create(
                model=model or "gpt-4",
                messages=[{"role": "user", "content": prompt}]
            )
            result = response.choices[0].message.content
            
        elif self.provider == "anthropic":
            message = self.client.messages.create(
                model=model or "claude-3-5-sonnet-latest",
                max_tokens=1024,
                messages=[{"role": "user", "content": prompt}]
            )
            result = message.content[0].text
            
        elif self.provider == "gemini":
            response = self.client.models.generate_content(
                model=model or 'gemini-2.0-flash-001',
                contents=prompt
            )
            result = response.text
        
        # Capture response
        capture_step(f"{self.provider}_response", 
                    {"model": model}, 
                    {"response": result}, 
                    self.process_id)
        
        return result

Key Advantages of Your Manual System

1. Complete Control: You decide exactly what to capture
2. Consistent Format: Same trace structure across all providers
3. No Dependencies: Doesn't rely on provider-specific tracing features
4. Easy Debugging: Simple JSON files you can inspect directly
5. Historical Comparison: Can compare traces across different LLM providers
6. Custom Metadata: Add any provider-specific or business logic data

Additional Enhancements You Could Add

1. Provider Comparison: Add provider info to traces for A/B testing
2. Cost Tracking: Add token counts and cost estimates per provider
3. Latency Metrics: Track response times across providers
4. Error Handling: Capture provider-specific errors consistently

Your manual tracing system is actually more flexible than provider-specific solutions because you built it yourself and control every aspect of it!

---

module1-opentelemetry-gm-1130.py follows:

---

uv venv

source .venv/bin/activate

uv pip install openai-agents

uv pip install python-dotenv

uv pip install opentelemetry-sdk opentelemetry-exporter-otlp opentelemetry-instrumentation

python module1.py # Input your goal or idea and get success criteria

python module2.py # Creates and selects a plan

python module3.py # Expands and evaluates the plan

python module3-claude.py # Better expands and evaluates the plan

python module3-gem.py # Expands and evaluates the plan

python module4.py # Identifies needed revisions

python module5.py # Implements revisions into a final plan

python module6.py # Generate easy to read markdown of final plan

import asyncio import json import os import logging import datetime import re from typing import Any, List, Dict, Optional import atexit

Import OpenAI libraries

from dotenv import load_dotenv from pydantic import BaseModel, Field, ValidationError, field_validator

from agents import ( Agent, GuardrailFunctionOutput, OutputGuardrail, Runner, WebSearchTool, trace, add_trace_processor, RunConfig ) from agents.run_context import RunContextWrapper from agents.lifecycle import AgentHooks from agents.tracing.processor_interface import TracingProcessor from agents.tracing.spans import Span from agents.tracing.traces import Trace from agents.tracing import span_data from agents.tracing import set_tracing_export_api_key

Add OpenTelemetry imports

from opentelemetry import trace as otel_trace from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter from opentelemetry.sdk.trace.export import SimpleSpanProcessor from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.resources import SERVICE_NAME, Resource from contextlib import contextmanager import uuid import copy

load_dotenv() # Load environment variables

Set tracing API key right after loading environment variables

api_key = os.getenv("OPENAI_API_KEY") if api_key: set_tracing_export_api_key(api_key) else: print("WARNING: OPENAI_API_KEY not found in environment variables")

--- Setup Logging (Modified for Verbosity) ---

def setup_logging(module_name): """Set up logging to console, a standard file, and a verbose file.""" logs_dir = os.path.join(os.path.dirname(os.path.abspath(file)), "logs") os.makedirs(logs_dir, exist_ok=True) timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") log_file = os.path.join(logs_dir, f"{module_name}_{timestamp}.log") verbose_log_file = os.path.join(logs_dir, f"{module_name}verbose{timestamp}.log")

logger = logging.getLogger(module_name)
logger.setLevel(logging.INFO)
if logger.handlers:
    logger.handlers = []

console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_format = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
console_handler.setFormatter(console_format)
logger.addHandler(console_handler)

file_handler = logging.FileHandler(log_file)
file_handler.setLevel(logging.INFO)
file_format = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
file_handler.setFormatter(file_format)
logger.addHandler(file_handler)

# Verbose logger (no truncation)
verbose_logger = logging.getLogger(f"{module_name}_verbose")
verbose_logger.setLevel(logging.INFO)
if verbose_logger.handlers:
    verbose_logger.handlers = []
verbose_file_handler = logging.FileHandler(verbose_log_file)
verbose_file_handler.setLevel(logging.INFO)
verbose_format = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
verbose_file_handler.setFormatter(verbose_format)
verbose_logger.addHandler(verbose_file_handler)

return logger, verbose_logger

Initialize loggers

logger, verbose_logger = setup_logging("module1")

Helper function to log to both loggers

def log_info(message, truncate=False, max_length=5000): verbose_logger.info(message) # Always log full message to verbose if truncate: if len(message) > max_length: message = message[:max_length] + "... [truncated, see verbose log]" logger.info(message) else: logger.info(message)

--- Text Validation Functions ---

def sanitize_text(text: str) -> str: """Clean and validate text to prevent corruption.""" if not isinstance(text, str): return str(text)

# Remove any non-printable or control characters
text = ''.join(char for char in text if char.isprintable() or char in ['\n', '\t', ' '])

# Check for obvious corruption patterns (random Unicode characters, etc.)
# This regex looks for clusters of non-English characters that might indicate corruption
corruption_pattern = r'[\u0400-\u04FF\u0600-\u06FF\u0900-\u097F\u3040-\u309F\u30A0-\u30FF\u3130-\u318F\uAC00-\uD7AF]{3,}'

# Replace corrupted sections with a note
text = re.sub(corruption_pattern, '[corrupted text removed]', text)

# Ensure the text doesn't exceed a reasonable size (50KB) - adjust as necessary
max_length = 50000
if len(text) > max_length:
    text = text[:max_length] + "...[text truncated due to length]"

return text

--- Pydantic Models --- (No changes)

class SuccessCriteria(BaseModel): criteria: str reasoning: str rating: int = Field(..., description="Rating of the criterion (1-10)")

@field_validator('rating')
def check_rating(cls, v):
    if not 1 <= v <= 10:
        raise ValueError('Rating must be between 1 and 10')
    return v

class Module1Output(BaseModel): goal: str success_criteria: list[SuccessCriteria] selected_criteria: list[SuccessCriteria] # Changed to a list for multiple criteria

@field_validator('selected_criteria')
def validate_selected_criteria(cls, v):
    if not v:
        raise ValueError("At least one criterion must be selected")
    return v

Global dictionary to store manual trace data

MANUAL_TRACES = { "traces": [], "current_trace": None }

def capture_step(stage, inputs, outputs, trace_id=None): """Explicitly capture workflow data at each significant point.""" global MANUAL_TRACES

# Create a new trace if needed
if MANUAL_TRACES["current_trace"] is None or trace_id != MANUAL_TRACES["current_trace"].get("id"):
    if trace_id is None:
        trace_id = f"manual_{uuid.uuid4().hex}"
        
    MANUAL_TRACES["current_trace"] = {
        "id": trace_id,
        "steps": [],
        "timestamp_start": datetime.datetime.now().isoformat()
    }
    MANUAL_TRACES["traces"].append(MANUAL_TRACES["current_trace"])

# Add the step data
step_data = {
    "stage": stage,
    "timestamp": datetime.datetime.now().isoformat(),
    "inputs": copy.deepcopy(inputs) if inputs else None,
    "outputs": copy.deepcopy(outputs) if outputs else None
}

MANUAL_TRACES["current_trace"]["steps"].append(step_data)

# Save to file
logs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs")
os.makedirs(logs_dir, exist_ok=True)
trace_file = os.path.join(logs_dir, "manual_traces.json")

try:
    with open(trace_file, "w") as f:
        json.dump(MANUAL_TRACES, f, indent=4, default=str)
except Exception as e:
    print(f"Error saving manual trace: {e}")

--- OpenTelemetry Setup ---

def setup_opentelemetry(service_name="OpenAI-Agents-Tracing"): """ Setup OpenTelemetry tracing with file and console exporters. Returns a tracer that can be used throughout the application. """ # Create logs directory for trace output files logs_dir = os.path.join(os.path.dirname(os.path.abspath(file)), "logs") os.makedirs(logs_dir, exist_ok=True)

# Create a timestamped filename for this run
timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
trace_file = os.path.join(logs_dir, f"otel_traces_{timestamp}.json")

# Create a resource to identify this service
resource = Resource(attributes={
    SERVICE_NAME: service_name
})

# Create a tracer provider with the resource
tracer_provider = TracerProvider(resource=resource)

# Set the global tracer provider
otel_trace.set_tracer_provider(tracer_provider)

# Create an enhanced JSON file exporter
class EnhancedJSONFileExporter:
    def __init__(self, filepath):
        self.filepath = filepath
        self.spans = []
        self.traces = {}  # Organize spans by trace_id
        
    def export(self, spans):
        for span in spans:
            # Get the trace ID
            trace_id = format(span.context.trace_id, '032x')
            
            # Convert span to JSON-serializable format
            span_data = {
                "name": span.name,
                "context": {
                    "trace_id": trace_id,
                    "span_id": format(span.context.span_id, '016x'),
                },
                "parent_id": format(span.parent.span_id, '016x') if span.parent else None,
                "start_time": span.start_time,
                "end_time": span.end_time,
                "duration_ms": (span.end_time - span.start_time) / 1000000,  # Convert to milliseconds
                "status": {
                    "status_code": span.status.status_code.name,
                    "description": span.status.description
                },
                "attributes": dict(span.attributes),
                "events": [
                    {
                        "name": event.name,
                        "timestamp": event.timestamp,
                        "attributes": dict(event.attributes)
                    } for event in span.events
                ]
            }
            
            # Add to spans list
            self.spans.append(span_data)
            
            # Organize by trace
            if trace_id not in self.traces:
                self.traces[trace_id] = {
                    "trace_id": trace_id,
                    "spans": [],
                    "start_time": span.start_time,  # Will be updated if earlier spans are added
                    "end_time": span.end_time,      # Will be updated if later spans are added
                }
            
            # Update trace timing if needed
            if span.start_time < self.traces[trace_id]["start_time"]:
                self.traces[trace_id]["start_time"] = span.start_time
            if span.end_time > self.traces[trace_id]["end_time"]:
                self.traces[trace_id]["end_time"] = span.end_time
                
            # Add span to its trace
            self.traces[trace_id]["spans"].append(span_data)
            
            # Calculate trace duration
            self.traces[trace_id]["duration_ms"] = (
                self.traces[trace_id]["end_time"] - self.traces[trace_id]["start_time"]
            ) / 1000000  # Convert to milliseconds
                
            # Write to file with each span for safety in two formats
            try:
                # Format 1: All spans in a flat list
                with open(self.filepath, 'w') as f:
                    json.dump({"spans": self.spans}, f, indent=4, default=str)
                    
                # Format 2: Spans organized by trace (more readable)
                trace_filepath = self.filepath.replace(".json", "_by_trace.json")
                with open(trace_filepath, 'w') as f:
                    json.dump({"traces": list(self.traces.values())}, f, indent=4, default=str)
            except Exception as e:
                print(f"Error writing span to file: {e}")
                
        return True
        
    def shutdown(self):
        # Final write - in case any spans were missed
        try:
            with open(self.filepath, 'w') as f:
                json.dump({"spans": self.spans}, f, indent=4, default=str)
                
            trace_filepath = self.filepath.replace(".json", "_by_trace.json")
            with open(trace_filepath, 'w') as f:
                json.dump({"traces": list(self.traces.values())}, f, indent=4, default=str)
        except Exception as e:
            print(f"Error during shutdown: {e}")

# Create and register exporters
json_exporter = EnhancedJSONFileExporter(trace_file)
console_exporter = ConsoleSpanExporter()  # Prints to stdout for debugging

# Add the exporters to the tracer provider
tracer_provider.add_span_processor(SimpleSpanProcessor(json_exporter))
tracer_provider.add_span_processor(SimpleSpanProcessor(console_exporter))

# Get a tracer from the provider
tracer = otel_trace.get_tracer(__name__)

log_info(f"OpenTelemetry initialized with trace file: {trace_file}")

return tracer

Context manager for creating rich spans with attributes

@contextmanager def traced_span(tracer, name, attributes=None, record_exception=True): """Context manager to create spans with rich attributes.""" attributes = attributes or {} with tracer.start_as_current_span(name) as span: # Set attributes on the span for key, value in attributes.items(): if isinstance(value, (dict, list)): # Convert complex objects to strings to ensure they can be serialized span.set_attribute(key, json.dumps(value)) else: span.set_attribute(key, str(value))

    try:
        yield span
    except Exception as e:
        if record_exception:
            # Record the exception in the span
            span.record_exception(e)
            span.set_status(otel_trace.StatusCode.ERROR, str(e))
        raise

class EnhancedFileTracingProcessor(TracingProcessor): """Custom processor to extract maximum content from traces and spans."""

def __init__(self, filename: str = "traces.json"):
    logs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs")
    os.makedirs(logs_dir, exist_ok=True)
    self.filename = os.path.join(logs_dir, filename)
    
    # Create a timestamped version
    timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    name, ext = os.path.splitext(filename)
    self.timestamped_filename = os.path.join(logs_dir, f"{name}_{timestamp}{ext}")
    
    # Data structures
    self.traces = {}  # Dict to store traces by trace_id
    self.spans = {}   # Dict to store spans by trace_id -> [spans]
    self.raw_content = {}  # Store raw content by span_id
    
    log_info(f"Enhanced FileTracingProcessor initialized with files: {self.filename} and {self.timestamped_filename}")

def on_trace_start(self, trace: Trace) -> None:
    """Store trace when it starts."""
    trace_id = trace.trace_id
    self.traces[trace_id] = trace
    self.spans.setdefault(trace_id, [])
    log_info(f"Trace started: {trace_id}", truncate=True)

def on_trace_end(self, trace: Trace) -> None:
    """Save completed trace and its spans."""
    trace_id = trace.trace_id
    self.traces[trace_id] = trace
    log_info(f"Trace ended: {trace_id}", truncate=True)
    self._save_to_file()

def on_span_start(self, span: Span[Any]) -> None:
    """Record span start."""
    trace_id = span.trace_id
    if trace_id not in self.spans:
        self.spans[trace_id] = []
    self._capture_span_content(span)

def on_span_end(self, span: Span[Any]) -> None:
    """Store finished span with all details."""
    trace_id = span.trace_id
    self._capture_span_content(span)
    if trace_id in self.spans:
        self.spans[trace_id].append(span)

def _capture_span_content(self, span: Span[Any]) -> None:
    """Extract maximum content from span using multiple approaches."""
    span_id = span.span_id
    span_data = {}
    
    # 1. Start with basic export
    exported = span.export()
    if exported:
        span_data.update(exported)
    
    # 2. Try to get the input data through direct attribute access
    if hasattr(span, "_input"):
        span_data["input"] = span._input
    elif hasattr(span, "input"):
        span_data["input"] = span.input
        
    # 3. Try to get output data
    if hasattr(span, "_output"):
        span_data["output"] = span._output
    elif hasattr(span, "output"):
        span_data["output"] = span.output
        
    # 4. Try to extract content from span_data based on type
    if hasattr(span, "span_data") and span.span_data:
        data_obj = span.span_data
        
        # Get type info
        span_type = None
        if hasattr(data_obj, "type"):
            span_type = data_obj.type
        elif "span_data" in span_data and "type" in span_data["span_data"]:
            span_type = span_data["span_data"]["type"]
            
        # Add all public attributes from span_data
        for attr_name in dir(data_obj):
            if not attr_name.startswith('_'):  # Public attributes only
                try:
                    attr_value = getattr(data_obj, attr_name)
                    if not callable(attr_value):  # Skip methods
                        span_data[f"span_data_{attr_name}"] = attr_value
                except Exception:
                    pass
                    
        # Type-specific extraction
        if span_type == "agent" or hasattr(data_obj, "agent_name") or hasattr(data_obj, "name"):
            for field in ["name", "agent_name", "instructions", "model", "tools", "handoffs"]:
                if hasattr(data_obj, field):
                    span_data[field] = getattr(data_obj, field)
                    
        elif span_type == "response" or hasattr(data_obj, "response_id"):
            for field in ["response_id", "content", "response", "request"]:
                if hasattr(data_obj, field):
                    span_data[field] = getattr(data_obj, field)
                    
        elif span_type == "function" or hasattr(data_obj, "function_name"):
            for field in ["function_name", "input", "output", "arguments", "result"]:
                if hasattr(data_obj, field):
                    span_data[field] = getattr(data_obj, field)
                    
        elif span_type == "generation" or hasattr(data_obj, "prompt"):
            for field in ["prompt", "response", "input_messages", "output", "model"]:
                if hasattr(data_obj, field):
                    span_data[field] = getattr(data_obj, field)
    
    # 5. Try to access span._data
    if hasattr(span, "_data"):
        try:
            for attr_name in dir(span._data):
                if not attr_name.startswith('_'):
                    try:
                        attr_value = getattr(span._data, attr_name)
                        if not callable(attr_value):
                            span_data[f"_data_{attr_name}"] = attr_value
                    except Exception:
                        pass
        except Exception:
            pass
            
    # 6. Try using vars() for a complete dump (might fail but worth trying)
    if hasattr(span, "span_data") and span.span_data:
        try:
            all_vars = vars(span.span_data)
            if all_vars:
                span_data["span_data_vars"] = all_vars
        except:
            pass
            
    # 7. Check for inner span data attributes that might contain content
    if hasattr(span, "span_data") and span.span_data:
        for inner_attr in ["data", "attributes", "content", "message", "messages"]:
            if hasattr(span.span_data, inner_attr):
                inner_value = getattr(span.span_data, inner_attr)
                span_data[f"inner_{inner_attr}"] = inner_value
    
    # Store the captured data
    self.raw_content[span_id] = span_data

def _save_to_file(self) -> None:
    """Save all completed traces and spans to file with full content."""
    try:
        # Prepare the data structure
        data = {"traces": []}
        
        for trace_id, trace in self.traces.items():
            trace_data = trace.export() or {}
            if not trace_data:
                continue
                
            # Add spans with enhanced content
            trace_data["spans"] = []
            for span in self.spans.get(trace_id, []):
                span_id = span.span_id
                span_data = self.raw_content.get(span_id, {})
                
                if not span_data:
                    span_data = span.export() or {}
                    
                if span_data:
                    trace_data["spans"].append(span_data)
            
            data["traces"].append(trace_data)
        
        # Add metadata
        data["timestamp"] = datetime.datetime.now().isoformat()
        data["count"] = len(data["traces"])
        
        # Save to both files
        for filename in [self.filename, self.timestamped_filename]:
            with open(filename, "w") as f:
                json.dump(data, f, indent=4, default=str)
            
        log_info(f"Saved {len(data['traces'])} traces with enhanced content", truncate=True)
        
    except Exception as e:
        logger.error(f"Error saving traces to file: {e}")
        verbose_logger.error(f"Error saving traces to file: {e}")
        import traceback
        logger.error(traceback.format_exc())

def shutdown(self) -> None:
    """Save final state when shutting down."""
    log_info("Shutting down EnhancedFileTracingProcessor, saving final traces", truncate=True)
    self._save_to_file()

def force_flush(self) -> None:
    """Force save current state."""
    self._save_to_file()

--- Custom Agent Hooks for Detailed Logging --- (Modified for verbosity)

class DetailedLoggingHooks(AgentHooks): def init(self, logger, verbose_logger, tracer=None, process_id=None): self.logger = logger self.verbose_logger = verbose_logger self.tracer = tracer # OpenTelemetry tracer self.process_id = process_id # For manual tracing

async def on_start(
    self, context: RunContextWrapper[Any], agent: Agent
):
    """Called before the agent is invoked."""
    log_info(f"===== API CALL: {agent.name} =====", truncate=True)
    log_info(f"Agent start: {agent.name}", truncate=True)
    self.verbose_logger.info(f"===== API CALL: {agent.name} =====")
    
    # Manual capture if process_id is available
    if self.process_id:
        agent_info = {
            "name": agent.name,
            "instructions": agent.instructions,
            "model": agent.model if hasattr(agent, "model") else "unknown",
            "tools": str(agent.tools) if hasattr(agent, "tools") else "none"
        }
        capture_step(f"agent_start_{agent.name}",
                   agent_info,
                   None,
                   self.process_id)
    
    # OpenTelemetry capture
    if self.tracer:
        current_span = otel_trace.get_current_span()
        if current_span:
            current_span.add_event(
                "Agent Start",
                {
                    "agent_name": agent.name,
                    "agent_instructions": agent.instructions if hasattr(agent, "instructions") else "none"
                }
            )
    return

async def on_end(
    self, context: RunContextWrapper[Any], agent: Agent, output: Any
):
    """Called when the agent produces a final output."""
    log_info(f"===== API RESPONSE: {agent.name} =====", truncate=True)
    self.verbose_logger.info(f"===== API RESPONSE: {agent.name} =====")

    try:
        # Sanitize output if needed
        if hasattr(output, 'final_output'):
            if isinstance(output.final_output, str):
                output.final_output = sanitize_text(output.final_output)
            elif isinstance(output.final_output, list):
                for item in output.final_output:
                    if hasattr(item, "criteria"):
                        item.criteria = sanitize_text(item.criteria)
                    if hasattr(item, "reasoning"):
                        item.reasoning = sanitize_text(item.reasoning)

            # Format output
            response_content = json.dumps(output.final_output, indent=2) if hasattr(output, 'final_output') else str(output)
            log_info(f"Response from {agent.name}: {response_content}", truncate=True)
            self.verbose_logger.info(f"Response from {agent.name}: {response_content}")
            
            # Manual capture
            if self.process_id:
                capture_step(f"agent_response_{agent.name}",
                           {"agent_name": agent.name},
                           {"response": output.final_output},
                           self.process_id)
            
            # OpenTelemetry capture
            if self.tracer:
                current_span = otel_trace.get_current_span()
                if current_span:
                    current_span.add_event(
                        "Agent Response",
                        {
                            "agent_name": agent.name,
                            "response": response_content[:5000]
                        }
                    )
        else:
            log_info(f"Response from {agent.name}: {str(output)}", truncate=True)
            self.verbose_logger.info(f"Response from {agent.name}: {str(output)}")
            
            # Manual capture
            if self.process_id:
                capture_step(f"agent_response_{agent.name}",
                           {"agent_name": agent.name},
                           {"response": str(output)},
                           self.process_id)
            
            # OpenTelemetry capture
            if self.tracer:
                current_span = otel_trace.get_current_span()
                if current_span:
                    current_span.add_event(
                        "Agent Response",
                        {
                            "agent_name": agent.name,
                            "response": str(output)[:5000]
                        }
                    )
    except Exception as e:
        log_info(f"Response from {agent.name}: {str(output)}", truncate=True)
        log_info(f"Could not format response as JSON: {e}", truncate=True)
        self.verbose_logger.info(f"Response from {agent.name}: {str(output)}")
        self.verbose_logger.info(f"Could not format response as JSON: {e}")
        
        # Manual capture of error
        if self.process_id:
            capture_step(f"agent_response_error_{agent.name}",
                       {"agent_name": agent.name},
                       {"error": str(e), "raw_output": str(output)},
                       self.process_id)
    return output

async def on_tool_start(
    self, context: RunContextWrapper[Any], agent: Agent, tool: Any
):
    """Called before a tool is invoked."""
    log_info(f"===== TOOL CALL: {agent.name} =====", truncate=True)
    self.verbose_logger.info(f"===== TOOL CALL: {agent.name} =====")
    
    # Get tool information
    tool_name = getattr(tool, "name", str(tool))
    tool_input = None
    if hasattr(tool, "_input"):
        tool_input = str(tool._input)
    
    # Manual capture
    if self.process_id:
        capture_step(f"tool_call_{agent.name}_{tool_name}",
                   {"agent_name": agent.name, "tool_name": tool_name},
                   {"tool_input": tool_input},
                   self.process_id)
    
    # OpenTelemetry capture
    if self.tracer:
        current_span = otel_trace.get_current_span()
        if current_span:
            current_span.add_event(
                "Tool Call",
                {
                    "agent_name": agent.name,
                    "tool_name": tool_name,
                    "tool_input": tool_input or "Unknown"
                }
            )
    return

async def on_tool_end(
    self, context: RunContextWrapper[Any], agent: Agent, tool: Any, result: str
):
    """Called after a tool is invoked."""
    tool_name = getattr(tool, "name", str(tool))
    
    try:
        response_content = json.dumps(result, indent=2)
        log_info(f"Tool Result from {agent.name}: {response_content}", truncate=True)
        self.verbose_logger.info(f"Tool Result from {agent.name}: {response_content}")
        
        # Manual capture
        if self.process_id:
            capture_step(f"tool_result_{agent.name}_{tool_name}",
                       {"agent_name": agent.name, "tool_name": tool_name},
                       {"result": result},
                       self.process_id)
        
        # OpenTelemetry capture
        if self.tracer:
            current_span = otel_trace.get_current_span()
            if current_span:
                current_span.add_event(
                    "Tool Result",
                    {
                        "agent_name": agent.name,
                        "tool_name": tool_name,
                        "result": response_content[:5000]
                    }
                )
    except Exception as e:
        log_info(f"Tool Result from {agent.name}: {str(result)}", truncate=True)
        self.verbose_logger.info(f"Tool Result from {agent.name}: {str(result)}")
        log_info(f"Could not format response as JSON: {e}", truncate=True)
        self.verbose_logger.info(f"Could not format response as JSON: {e}")
        
        # Manual capture of error
        if self.process_id:
            capture_step(f"tool_result_error_{agent.name}_{tool_name}",
                       {"agent_name": agent.name, "tool_name": tool_name},
                       {"error": str(e), "raw_result": str(result)},
                       self.process_id)
        
        # OpenTelemetry capture
        if self.tracer:
            current_span = otel_trace.get_current_span()
            if current_span:
                current_span.add_event(
                    "Tool Result Error",
                    {
                        "agent_name": agent.name,
                        "tool_name": tool_name,
                        "raw_result": str(result)[:5000],
                        "error": str(e)
                    }
                )

    return result

--- Search Agent ---

web_search_tool = WebSearchTool() # Instantiate the tool

Note: we'll set the logging_hooks with tracer later

search_agent = Agent( name="SearchAgent", instructions=( "You are a web search assistant. Given a user's goal, " "perform a web search to find information relevant to defining success criteria. " "Return a concise summary of your findings, including citations to sources." ), model="gpt-4o", tools=[web_search_tool], # Pass the instance of the tool # hooks=logging_hooks, # We'll set this in main() )

--- Other Agents ---

generate_criteria_agent = Agent( name="CriteriaGenerator", instructions=( "You are a helpful assistant. Given a user's goal or idea, and the results of a web search," "generate five distinct and measurable success criteria. " "Provide a brief reasoning for each criterion. " "Rate each criterion on a scale of 1-10 based on how strongly it indicates goal achievement." ), model="gpt-4o", output_type=list[SuccessCriteria], # hooks=logging_hooks, # We'll set this in main() )

evaluate_criteria_agent = Agent( name="CriteriaEvaluator", instructions=( "You are an expert evaluator. Given a goal/idea, search results, and a list of " "potential success criteria, select the THREE criteria that, if met together, " "would most strongly indicate that the goal has been achieved. " "Choose criteria that complement each other and cover different aspects of the goal. " "Consider information found by search to assist with your selection. " "Provide detailed reasoning for each of your selections." ), model="gpt-4o", output_type=list[SuccessCriteria], # Changed to expect a list # hooks=logging_hooks, # We'll set this in main() )

async def validate_module1_output( context: RunContextWrapper[None], agent: Agent, agent_output: Any ) -> GuardrailFunctionOutput: """Validates the output of Module 1.""" try: log_info("Validating Module 1 output...", truncate=True) verbose_logger.info("Validating Module 1 output...")

    # Log only key parts for the standard log
    truncated_output = {
        "goal": agent_output.goal,
        "selected_criteria_count": len(agent_output.selected_criteria),
    }

    log_info(f"Output to validate (truncated): {json.dumps(truncated_output, indent=2)}", truncate=True)
    verbose_logger.info(f"Output to validate: {json.dumps(agent_output.model_dump() if hasattr(agent_output, 'model_dump') else agent_output, indent=2)}")

    Module1Output.model_validate(agent_output)
    log_info("Module 1 output validation passed", truncate=True)
    verbose_logger.info("Module 1 output validation passed")
    return GuardrailFunctionOutput(output_info=None, tripwire_triggered=False)
except ValidationError as e:
    logger.error(f"Module 1 output validation failed: {e}")
    verbose_logger.error(f"Module 1 output validation failed: {e}")
    return GuardrailFunctionOutput(
        output_info={"error": str(e)}, tripwire_triggered=True
    )

async def run_module_1(user_goal: str, output_file: str, tracer, process_id: str) -> None: """Runs Module 1 with comprehensive tracing.""" # Start OpenTelemetry span for the module with traced_span(tracer, "run_module_1", {"user_goal": user_goal}) as module_span: context = RunContextWrapper(context=None) openai_trace_id = None

    try:
        log_info(f"Starting Module 1 with goal: {user_goal}", truncate=True)
        verbose_logger.info(f"Starting Module 1 with goal: {user_goal}")
        
        # Manual capture for module start
        capture_step("module_start", {"user_goal": user_goal}, None, process_id)

        # --- Wrap the entire module execution in a trace ---
        with trace("Module 1 Trace") as current_trace:
            # Get the OpenAI trace ID if available
            if hasattr(current_trace, "trace_id"):
                openai_trace_id = current_trace.trace_id
                module_span.set_attribute("openai_trace_id", openai_trace_id)
                # Link manual trace to OpenAI trace
                capture_step("trace_linked", {"openai_trace_id": openai_trace_id}, None, process_id)
            
            # --- Run Search Agent ---
            with traced_span(tracer, "search_agent", {"agent": "SearchAgent"}) as search_span:
                log_info("Running Search Agent...", truncate=True)
                verbose_logger.info("Running Search Agent...")
                
                # Manual capture before search
                search_input = f"Find information about success criteria for: {user_goal}"
                capture_step("search_start", {"input": search_input}, None, process_id)

                # Create RunConfig with trace_include_sensitive_data=True
                run_config = RunConfig(trace_include_sensitive_data=True)

                try:
                    search_result = await Runner.run(
                        search_agent,
                        input=search_input,
                        context=context,
                        run_config=run_config,  # Include sensitive data
                    )
                    search_summary = search_result.final_output
                    
                    # Manual capture after search
                    capture_step("search_complete", {"input": search_input}, {"search_summary": search_summary}, process_id)
                    
                    # OpenTelemetry capture
                    search_span.set_attribute("search_success", "true")
                    search_span.set_attribute("summary_length", str(len(search_summary)) if search_summary else "0")
                    if search_summary:
                        search_span.set_attribute("summary_excerpt", search_summary[:1000] if len(search_summary) > 1000 else search_summary)
                    
                    log_info(f"Search Agent returned (truncated): {search_summary[:200]}...", truncate=True)
                    verbose_logger.info(f"Search Agent returned (full): {search_summary}")

                except Exception as e:
                    logger.warning(f"Search Agent failed: {e}. Proceeding without search results.")
                    verbose_logger.warning(f"Search Agent failed: {e}. Proceeding without search results.")
                    search_summary = "No search results available."  # Fallback message
                    
                    # Manual capture of failure
                    capture_step("search_failed", {"input": search_input}, {"error": str(e)}, process_id)
                    
                    # Record in OpenTelemetry
                    search_span.record_exception(e)
                    search_span.set_status(otel_trace.StatusCode.ERROR, str(e))
                    search_span.set_attribute("search_success", "false")

            # --- Generate criteria (with search results) ---
            with traced_span(tracer, "criteria_generation", {"agent": "CriteriaGenerator"}) as criteria_span:
                log_info("GENERATING CANDIDATE SUCCESS CRITERIA...", truncate=True)
                verbose_logger.info("GENERATING CANDIDATE SUCCESS CRITERIA...")
                
                # Prepare the input
                criteria_input = f"The user's goal is: {user_goal}\n\nSearch Results:\n{search_summary}"
                
                # Manual capture
                capture_step("criteria_generation_start", {"input": criteria_input}, None, process_id)
                
                # OpenTelemetry capture
                criteria_span.set_attribute("input", criteria_input)

                # Run with trace_include_sensitive_data=True
                criteria_result = await Runner.run(
                    generate_criteria_agent,
                    input=criteria_input,
                    context=context,
                    run_config=run_config,  # Include sensitive data
                )
                generated_criteria = criteria_result.final_output
                
                # Manual capture of results
                capture_step("criteria_generation_complete", 
                            {"input": criteria_input}, 
                            {"generated_criteria": [c.model_dump() for c in generated_criteria]}, 
                            process_id)
                
                # OpenTelemetry capture
                criteria_span.set_attribute("criteria_count", str(len(generated_criteria)))
                criteria_summary = [{"criteria": c.criteria, "rating": c.rating} for c in generated_criteria]
                criteria_span.set_attribute("criteria_summary", json.dumps(criteria_summary))
                
                log_info(f"Generated {len(generated_criteria)} success criteria", truncate=True)
                verbose_logger.info(f"Generated {len(generated_criteria)} success criteria")

                # Log each criterion
                for i, criterion in enumerate(generated_criteria, 1):
                    log_info(f"Criterion {i}: {criterion.criteria} (Rating: {criterion.rating})", truncate=True)
                    verbose_logger.info(f"Criterion {i}: {criterion.criteria} (Rating: {criterion.rating})")

            # Select top criteria
            with traced_span(tracer, "criteria_evaluation", {"agent": "CriteriaEvaluator"}) as eval_span:
                log_info("EVALUATING AND SELECTING TOP CRITERIA...", truncate=True)
                verbose_logger.info("EVALUATING AND SELECTING TOP CRITERIA...")

                # Format criteria for the evaluator
                criteria_json = json.dumps([c.model_dump() for c in generated_criteria], indent=2)
                evaluation_input = (
                    f"Goal: {user_goal}\n\nSearch Results:\n{search_summary}\n\nCriteria:\n{criteria_json}"
                )
                
                # Manual capture
                capture_step("criteria_evaluation_start", {"input": evaluation_input}, None, process_id)
                
                # OpenTelemetry capture
                eval_span.set_attribute("input_length", str(len(evaluation_input)))
                
                log_info(f"Evaluation input (truncated): {evaluation_input[:500]}...", truncate=True)
                verbose_logger.info(f"Evaluation input (full): {evaluation_input}")

                # Run evaluation
                evaluation_result = await Runner.run(
                    evaluate_criteria_agent,
                    input=evaluation_input,
                    context=context,
                    run_config=run_config,  # Include sensitive data
                )
                selected_criteria = evaluation_result.final_output
                
                # Manual capture
                capture_step("criteria_evaluation_complete", 
                           {"input": evaluation_input}, 
                           {"selected_criteria": [c.model_dump() for c in selected_criteria]}, 
                           process_id)
                
                # OpenTelemetry capture
                eval_span.set_attribute("selected_count", str(len(selected_criteria)))
                selected_summary = [{"criteria": c.criteria, "rating": c.rating} for c in selected_criteria]
                eval_span.set_attribute("selected_criteria", json.dumps(selected_summary))
                
                log_info(f"Selected {len(selected_criteria)} top criteria", truncate=True)
                verbose_logger.info(f"Selected {len(selected_criteria)} top criteria")

                # Log selected criteria
                for i, criterion in enumerate(selected_criteria, 1):
                    log_info(f"Selected Criterion {i}: {criterion.criteria} (Rating: {criterion.rating})", truncate=True)
                    verbose_logger.info(f"Selected Criterion {i}: {criterion.criteria} (Rating: {criterion.rating})")

            # Create output with guardrail check
            with traced_span(tracer, "output_creation", {"output_file": output_file}) as output_span:
                # Create the output object using Pydantic
                log_info("CREATING MODULE 1 OUTPUT OBJECT...", truncate=True)
                verbose_logger.info("CREATING MODULE 1 OUTPUT OBJECT...")

                module_1_output = Module1Output(
                    goal=user_goal,
                    success_criteria=generated_criteria,
                    selected_criteria=selected_criteria,  # Multiple criteria
                )
                
                # Manual capture
                capture_step("module_output_created", 
                           {"goal": user_goal}, 
                           {"module_1_output": module_1_output.model_dump()}, 
                           process_id)

                # Log the complete output (only to verbose log)
                verbose_logger.info(f"Complete Module 1 output: {json.dumps(module_1_output.model_dump(), indent=2)}")

                # Add the output guardrail
                log_info("Applying output guardrail...", truncate=True)
                verbose_logger.info("Applying output guardrail...")
                
                # Manual capture
                capture_step("guardrail_start", {"module_1_output": module_1_output.model_dump()}, None, process_id)

                guardrail = OutputGuardrail(guardrail_function=validate_module1_output)
                guardrail_result = await guardrail.run(
                    agent=evaluate_criteria_agent,
                    agent_output=module_1_output,
                    context=context,
                    run_config=run_config,  # Include sensitive data
                )
                
                # Manual capture
                guardrail_output = {
                    "tripwire_triggered": guardrail_result.output.tripwire_triggered,
                    "output_info": guardrail_result.output.output_info
                }
                capture_step("guardrail_complete", 
                           {"module_1_output": module_1_output.model_dump()}, 
                           guardrail_output, 
                           process_id)
                
                # OpenTelemetry capture
                output_span.set_attribute("guardrail_triggered", str(guardrail_result.output.tripwire_triggered))
                if guardrail_result.output.output_info:
                    output_span.set_attribute("guardrail_info", str(guardrail_result.output.output_info))

                if guardrail_result.output.tripwire_triggered:
                    logger.error(f"Guardrail failed: {guardrail_result.output.output_info}")
                    verbose_logger.error(f"Guardrail failed: {guardrail_result.output.output_info}")
                    output_span.set_status(otel_trace.StatusCode.ERROR, "Guardrail triggered")
                    return

                # --- Smart JSON Export ---
                log_info("Exporting output to files...", truncate=True)
                verbose_logger.info("Exporting output to files...")
                
                # Create data directory if it doesn't exist
                output_dir = os.path.dirname(output_file)
                os.makedirs(output_dir, exist_ok=True)
                
                # Create timestamped version
                timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
                filename = os.path.basename(output_file)
                name, ext = os.path.splitext(filename)
                timestamped_file = os.path.join(output_dir, f"{name}_{timestamp}{ext}")
                
                # Manual capture
                capture_step("file_export", 
                           {"module_1_output": module_1_output.model_dump()}, 
                           {"output_file": output_file, "timestamped_file": timestamped_file}, 
                           process_id)
                
                # Export both versions
                with open(output_file, "w") as f:
                    json.dump(module_1_output.model_dump(), f, indent=4)
                with open(timestamped_file, "w") as f:
                    json.dump(module_1_output.model_dump(), f, indent=4)
                
                # Final manual capture
                capture_step("module_complete", 
                           {"trace_id": openai_trace_id}, 
                           {"status": "success", "files_saved": [output_file, timestamped_file]}, 
                           process_id)
                
                log_info(f"Module 1 completed. Output saved to {output_file}", truncate=True)
                log_info(f"Timestamped output saved to {timestamped_file}", truncate=True)
                verbose_logger.info(f"Module 1 completed. Output saved to {output_file}")
                verbose_logger.info(f"Timestamped output saved to {timestamped_file}")

    except Exception as e:
        logger.error(f"An error occurred in Module 1: {e}")
        verbose_logger.error(f"An error occurred in Module 1: {e}")
        import traceback
        error_trace = traceback.format_exc()
        logger.error(error_trace)
        verbose_logger.error(error_trace)  # Log the full stack trace
        
        # Manual capture of error
        capture_step("module_error", 
                   {"user_goal": user_goal}, 
                   {"error": str(e), "traceback": error_trace}, 
                   process_id)
        
        # Record in OpenTelemetry
        module_span.record_exception(e)
        module_span.set_status(otel_trace.StatusCode.ERROR, str(e))

async def main(): log_info("Starting main function", truncate=True) verbose_logger.info("Starting main function")

# Initialize OpenTelemetry
tracer = setup_opentelemetry()

# Create a manual trace for the entire process
process_id = f"process_{uuid.uuid4().hex}"
capture_step("process_start", {"timestamp": datetime.datetime.now().isoformat()}, None, process_id)

# Start an OpenTelemetry span for the entire process
with tracer.start_as_current_span("module1_execution") as parent_span:
    user_goal = input("Please enter your goal or idea: ")
    parent_span.set_attribute("user_goal", user_goal)
    capture_step("user_input", None, {"user_goal": user_goal}, process_id)
    
    log_info(f"User input goal: {user_goal}", truncate=True)
    verbose_logger.info(f"User input goal: {user_goal}")

    output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
    os.makedirs(output_dir, exist_ok=True)
    output_file = os.path.join(output_dir, "module1_output.json")

    # Set up both tracing systems
    file_processor = EnhancedFileTracingProcessor()
    add_trace_processor(file_processor)
    
    # Verify API key is set
    api_key = os.getenv("OPENAI_API_KEY")
    if api_key:
        log_info(f"API key found (length: {len(api_key)})", truncate=True)
        parent_span.set_attribute("api_key_found", "true")
    else:
        log_info("WARNING: OPENAI_API_KEY not found in environment variables", truncate=True)
        parent_span.set_attribute("api_key_found", "false")

    # Add cleanup at exit
    atexit.register(file_processor.shutdown)

    # Update logging hooks with the tracer AND process_id
    global logging_hooks
    logging_hooks = DetailedLoggingHooks(logger, verbose_logger, tracer, process_id)

    # Update agents with new hooks that have tracer AND process_id
    search_agent.hooks = logging_hooks
    generate_criteria_agent.hooks = logging_hooks
    evaluate_criteria_agent.hooks = logging_hooks

    
    # Run module 1 with tracing
    await run_module_1(user_goal, output_file, tracer, process_id)
    
    # Final capture for manual trace
    capture_step("process_complete", {"timestamp": datetime.datetime.now().isoformat()}, None, process_id)
    
    log_info("Main function completed", truncate=True)
    verbose_logger.info("Main function completed")

if name == "main": log_info("Module 1 script starting", truncate=True) verbose_logger.info("Module 1 script starting")

asyncio.run(main())
log_info("Module 1 script completed", truncate=True)
verbose_logger.info("Module 1 script completed")

---

Example exported traces from above module1-opentelemetry-gm-1130.py code follows:

Filename: manual_traces.json

---

{ "traces": [ { "id": "process_7681415e10c746958b5dab58d8aa1f4b", "steps": [ { "stage": "process_start", "timestamp": "2025-03-22T22:06:35.051053", "inputs": { "timestamp": "2025-03-22T22:06:35.051048" }, "outputs": null }, { "stage": "user_input", "timestamp": "2025-03-22T22:06:56.111179", "inputs": null, "outputs": { "user_goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." } }, { "stage": "module_start", "timestamp": "2025-03-22T22:06:56.113833", "inputs": { "user_goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." }, "outputs": null }, { "stage": "trace_linked", "timestamp": "2025-03-22T22:06:56.114997", "inputs": { "openai_trace_id": "trace_938a54300e604328a1ef6c1c0d9278f5" }, "outputs": null }, { "stage": "search_start", "timestamp": "2025-03-22T22:06:56.115854", "inputs": { "input": "Find information about success criteria for: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." }, "outputs": null }, { "stage": "agent_start_SearchAgent", "timestamp": "2025-03-22T22:06:56.118632", "inputs": { "name": "SearchAgent", "instructions": "You are a web search assistant. Given a user's goal, perform a web search to find information relevant to defining success criteria. Return a concise summary of your findings, including citations to sources.", "model": "gpt-4o", "tools": "[WebSearchTool(user_location=None, search_context_size='medium')]" }, "outputs": null }, { "stage": "agent_response_SearchAgent", "timestamp": "2025-03-22T22:07:04.448615", "inputs": { "agent_name": "SearchAgent" }, "outputs": { "response": "To develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. " } }, { "stage": "search_complete", "timestamp": "2025-03-22T22:07:04.449750", "inputs": { "input": "Find information about success criteria for: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." }, "outputs": { "search_summary": "To develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. " } }, { "stage": "criteria_generation_start", "timestamp": "2025-03-22T22:07:04.452182", "inputs": { "input": "The user's goal is: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.\n\nSearch Results:\nTo develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. " }, "outputs": null }, { "stage": "agent_start_CriteriaGenerator", "timestamp": "2025-03-22T22:07:04.457842", "inputs": { "name": "CriteriaGenerator", "instructions": "You are a helpful assistant. Given a user's goal or idea, and the results of a web search,generate five distinct and measurable success criteria. Provide a brief reasoning for each criterion. Rate each criterion on a scale of 1-10 based on how strongly it indicates goal achievement.", "model": "gpt-4o", "tools": "[]" }, "outputs": null }, { "stage": "agent_response_CriteriaGenerator", "timestamp": "2025-03-22T22:07:07.902576", "inputs": { "agent_name": "CriteriaGenerator" }, "outputs": { "response": "[SuccessCriteria(criteria='Clarity and Relevance of Workshop Content', reasoning='The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.', rating=9), SuccessCriteria(criteria='Effectiveness of Scenario Utilization', reasoning='The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.', rating=8), SuccessCriteria(criteria='Engagement and Participation Levels', reasoning="Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.", rating=7), SuccessCriteria(criteria='Comprehensiveness of Brainstorming Outcomes', reasoning="The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.", rating=8), SuccessCriteria(criteria='Post-Workshop Assessment Scores', reasoning='Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.', rating=9)]" } }, { "stage": "criteria_generation_complete", "timestamp": "2025-03-22T22:07:07.904341", "inputs": { "input": "The user's goal is: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.\n\nSearch Results:\nTo develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. " }, "outputs": { "generated_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.", "rating": 8 }, { "criteria": "Engagement and Participation Levels", "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.", "rating": 7 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.", "rating": 8 }, { "criteria": "Post-Workshop Assessment Scores", "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.", "rating": 9 } ] } }, { "stage": "criteria_evaluation_start", "timestamp": "2025-03-22T22:07:07.908521", "inputs": { "input": "Goal: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.\n\nSearch Results:\nTo develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. \n\nCriteria:\n[\n {\n "criteria": "Clarity and Relevance of Workshop Content",\n "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.",\n "rating": 9\n },\n {\n "criteria": "Effectiveness of Scenario Utilization",\n "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.",\n "rating": 8\n },\n {\n "criteria": "Engagement and Participation Levels",\n "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.",\n "rating": 7\n },\n {\n "criteria": "Comprehensiveness of Brainstorming Outcomes",\n "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.",\n "rating": 8\n },\n {\n "criteria": "Post-Workshop Assessment Scores",\n "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.",\n "rating": 9\n }\n]" }, "outputs": null }, { "stage": "agent_start_CriteriaEvaluator", "timestamp": "2025-03-22T22:07:07.914424", "inputs": { "name": "CriteriaEvaluator", "instructions": "You are an expert evaluator. Given a goal/idea, search results, and a list of potential success criteria, select the THREE criteria that, if met together, would most strongly indicate that the goal has been achieved. Choose criteria that complement each other and cover different aspects of the goal. Consider information found by search to assist with your selection. Provide detailed reasoning for each of your selections.", "model": "gpt-4o", "tools": "[]" }, "outputs": null }, { "stage": "agent_response_CriteriaEvaluator", "timestamp": "2025-03-22T22:07:10.149490", "inputs": { "agent_name": "CriteriaEvaluator" }, "outputs": { "response": "[SuccessCriteria(criteria='Clarity and Relevance of Workshop Content', reasoning='The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. This criterion ensures the content aligns with the goal, addressing key areas like liability, authority, and fiduciary duties specifically tied to AI agents and transactions.', rating=9), SuccessCriteria(criteria='Effectiveness of Scenario Utilization', reasoning='A well-crafted scenario is crucial for participants to visualize and apply the discussed principles in a practical context. This helps bridge theoretical knowledge with real-world applications, making the workshop more impactful and providing a tangible framework for brainstorming relevant contractual provisions.', rating=8), SuccessCriteria(criteria='Comprehensiveness of Brainstorming Outcomes', reasoning="The depth and breadth of the contractual provisions identified during brainstorming sessions indicate the participants' grasp of the material. Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", rating=8)]" } }, { "stage": "criteria_evaluation_complete", "timestamp": "2025-03-22T22:07:10.151900", "inputs": { "input": "Goal: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.\n\nSearch Results:\nTo develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. \n\nCriteria:\n[\n {\n "criteria": "Clarity and Relevance of Workshop Content",\n "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.",\n "rating": 9\n },\n {\n "criteria": "Effectiveness of Scenario Utilization",\n "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.",\n "rating": 8\n },\n {\n "criteria": "Engagement and Participation Levels",\n "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.",\n "rating": 7\n },\n {\n "criteria": "Comprehensiveness of Brainstorming Outcomes",\n "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.",\n "rating": 8\n },\n {\n "criteria": "Post-Workshop Assessment Scores",\n "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.",\n "rating": 9\n }\n]" }, "outputs": { "selected_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. This criterion ensures the content aligns with the goal, addressing key areas like liability, authority, and fiduciary duties specifically tied to AI agents and transactions.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "A well-crafted scenario is crucial for participants to visualize and apply the discussed principles in a practical context. This helps bridge theoretical knowledge with real-world applications, making the workshop more impactful and providing a tangible framework for brainstorming relevant contractual provisions.", "rating": 8 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The depth and breadth of the contractual provisions identified during brainstorming sessions indicate the participants' grasp of the material. Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", "rating": 8 } ] } }, { "stage": "module_output_created", "timestamp": "2025-03-22T22:07:10.157824", "inputs": { "goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." }, "outputs": { "module_1_output": { "goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.", "success_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.", "rating": 8 }, { "criteria": "Engagement and Participation Levels", "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.", "rating": 7 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.", "rating": 8 }, { "criteria": "Post-Workshop Assessment Scores", "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.", "rating": 9 } ], "selected_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. This criterion ensures the content aligns with the goal, addressing key areas like liability, authority, and fiduciary duties specifically tied to AI agents and transactions.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "A well-crafted scenario is crucial for participants to visualize and apply the discussed principles in a practical context. This helps bridge theoretical knowledge with real-world applications, making the workshop more impactful and providing a tangible framework for brainstorming relevant contractual provisions.", "rating": 8 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The depth and breadth of the contractual provisions identified during brainstorming sessions indicate the participants' grasp of the material. Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", "rating": 8 } ] } } }, { "stage": "guardrail_start", "timestamp": "2025-03-22T22:07:10.160421", "inputs": { "module_1_output": { "goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.", "success_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.", "rating": 8 }, { "criteria": "Engagement and Participation Levels", "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.", "rating": 7 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.", "rating": 8 }, { "criteria": "Post-Workshop Assessment Scores", "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.", "rating": 9 } ], "selected_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. This criterion ensures the content aligns with the goal, addressing key areas like liability, authority, and fiduciary duties specifically tied to AI agents and transactions.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "A well-crafted scenario is crucial for participants to visualize and apply the discussed principles in a practical context. This helps bridge theoretical knowledge with real-world applications, making the workshop more impactful and providing a tangible framework for brainstorming relevant contractual provisions.", "rating": 8 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The depth and breadth of the contractual provisions identified during brainstorming sessions indicate the participants' grasp of the material. Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", "rating": 8 } ] } }, "outputs": null }, { "stage": "guardrail_complete", "timestamp": "2025-03-22T22:07:10.164748", "inputs": { "module_1_output": { "goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.", "success_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.", "rating": 8 }, { "criteria": "Engagement and Participation Levels", "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.", "rating": 7 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.", "rating": 8 }, { "criteria": "Post-Workshop Assessment Scores", "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.", "rating": 9 } ], "selected_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. This criterion ensures the content aligns with the goal, addressing key areas like liability, authority, and fiduciary duties specifically tied to AI agents and transactions.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "A well-crafted scenario is crucial for participants to visualize and apply the discussed principles in a practical context. This helps bridge theoretical knowledge with real-world applications, making the workshop more impactful and providing a tangible framework for brainstorming relevant contractual provisions.", "rating": 8 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The depth and breadth of the contractual provisions identified during brainstorming sessions indicate the participants' grasp of the material. Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", "rating": 8 } ] } }, "outputs": { "tripwire_triggered": false, "output_info": null } }, { "stage": "file_export", "timestamp": "2025-03-22T22:07:10.167355", "inputs": { "module_1_output": { "goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.", "success_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.", "rating": 8 }, { "criteria": "Engagement and Participation Levels", "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.", "rating": 7 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.", "rating": 8 }, { "criteria": "Post-Workshop Assessment Scores", "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.", "rating": 9 } ], "selected_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. 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Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", "rating": 8 } ] } }, "outputs": { "output_file": "/Users/dazzagreenwood/agento6/data/module1_output.json", "timestamped_file": "/Users/dazzagreenwood/agento6/data/module1_output_2025-03-22_22-07-10.json" } }, { "stage": "module_complete", "timestamp": "2025-03-22T22:07:10.170535", "inputs": { "trace_id": "trace_938a54300e604328a1ef6c1c0d9278f5" }, "outputs": { "status": "success", "files_saved": [ "/Users/dazzagreenwood/agento6/data/module1_output.json", "/Users/dazzagreenwood/agento6/data/module1_output_2025-03-22_22-07-10.json" ] } }, { "stage": "process_complete", "timestamp": "2025-03-22T22:07:10.176303", "inputs": { "timestamp": "2025-03-22T22:07:10.176298" }, "outputs": null } ], "timestamp_start": "2025-03-22T22:06:35.051052" } ], "current_trace": { "id": "process_7681415e10c746958b5dab58d8aa1f4b", "steps": [ { "stage": "process_start", "timestamp": "2025-03-22T22:06:35.051053", "inputs": { "timestamp": "2025-03-22T22:06:35.051048" }, "outputs": null }, { "stage": "user_input", "timestamp": "2025-03-22T22:06:56.111179", "inputs": null, "outputs": { "user_goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." } }, { "stage": "module_start", "timestamp": "2025-03-22T22:06:56.113833", "inputs": { "user_goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." }, "outputs": null }, { "stage": "trace_linked", "timestamp": "2025-03-22T22:06:56.114997", "inputs": { "openai_trace_id": "trace_938a54300e604328a1ef6c1c0d9278f5" }, "outputs": null }, { "stage": "search_start", "timestamp": "2025-03-22T22:06:56.115854", "inputs": { "input": "Find information about success criteria for: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." }, "outputs": null }, { "stage": "agent_start_SearchAgent", "timestamp": "2025-03-22T22:06:56.118632", "inputs": { "name": "SearchAgent", "instructions": "You are a web search assistant. Given a user's goal, perform a web search to find information relevant to defining success criteria. Return a concise summary of your findings, including citations to sources.", "model": "gpt-4o", "tools": "[WebSearchTool(user_location=None, search_context_size='medium')]" }, "outputs": null }, { "stage": "agent_response_SearchAgent", "timestamp": "2025-03-22T22:07:04.448615", "inputs": { "agent_name": "SearchAgent" }, "outputs": { "response": "To develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. " } }, { "stage": "search_complete", "timestamp": "2025-03-22T22:07:04.449750", "inputs": { "input": "Find information about success criteria for: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." }, "outputs": { "search_summary": "To develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. " } }, { "stage": "criteria_generation_start", "timestamp": "2025-03-22T22:07:04.452182", "inputs": { "input": "The user's goal is: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.\n\nSearch Results:\nTo develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. " }, "outputs": null }, { "stage": "agent_start_CriteriaGenerator", "timestamp": "2025-03-22T22:07:04.457842", "inputs": { "name": "CriteriaGenerator", "instructions": "You are a helpful assistant. Given a user's goal or idea, and the results of a web search,generate five distinct and measurable success criteria. Provide a brief reasoning for each criterion. Rate each criterion on a scale of 1-10 based on how strongly it indicates goal achievement.", "model": "gpt-4o", "tools": "[]" }, "outputs": null }, { "stage": "agent_response_CriteriaGenerator", "timestamp": "2025-03-22T22:07:07.902576", "inputs": { "agent_name": "CriteriaGenerator" }, "outputs": { "response": "[SuccessCriteria(criteria='Clarity and Relevance of Workshop Content', reasoning='The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.', rating=9), SuccessCriteria(criteria='Effectiveness of Scenario Utilization', reasoning='The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.', rating=8), SuccessCriteria(criteria='Engagement and Participation Levels', reasoning="Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.", rating=7), SuccessCriteria(criteria='Comprehensiveness of Brainstorming Outcomes', reasoning="The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.", rating=8), SuccessCriteria(criteria='Post-Workshop Assessment Scores', reasoning='Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.', rating=9)]" } }, { "stage": "criteria_generation_complete", "timestamp": "2025-03-22T22:07:07.904341", "inputs": { "input": "The user's goal is: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.\n\nSearch Results:\nTo develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. " }, "outputs": { "generated_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.", "rating": 8 }, { "criteria": "Engagement and Participation Levels", "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.", "rating": 7 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.", "rating": 8 }, { "criteria": "Post-Workshop Assessment Scores", "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.", "rating": 9 } ] } }, { "stage": "criteria_evaluation_start", "timestamp": "2025-03-22T22:07:07.908521", "inputs": { "input": "Goal: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.\n\nSearch Results:\nTo develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. \n\nCriteria:\n[\n {\n "criteria": "Clarity and Relevance of Workshop Content",\n "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.",\n "rating": 9\n },\n {\n "criteria": "Effectiveness of Scenario Utilization",\n "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.",\n "rating": 8\n },\n {\n "criteria": "Engagement and Participation Levels",\n "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.",\n "rating": 7\n },\n {\n "criteria": "Comprehensiveness of Brainstorming Outcomes",\n "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.",\n "rating": 8\n },\n {\n "criteria": "Post-Workshop Assessment Scores",\n "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.",\n "rating": 9\n }\n]" }, "outputs": null }, { "stage": "agent_start_CriteriaEvaluator", "timestamp": "2025-03-22T22:07:07.914424", "inputs": { "name": "CriteriaEvaluator", "instructions": "You are an expert evaluator. Given a goal/idea, search results, and a list of potential success criteria, select the THREE criteria that, if met together, would most strongly indicate that the goal has been achieved. Choose criteria that complement each other and cover different aspects of the goal. Consider information found by search to assist with your selection. Provide detailed reasoning for each of your selections.", "model": "gpt-4o", "tools": "[]" }, "outputs": null }, { "stage": "agent_response_CriteriaEvaluator", "timestamp": "2025-03-22T22:07:10.149490", "inputs": { "agent_name": "CriteriaEvaluator" }, "outputs": { "response": "[SuccessCriteria(criteria='Clarity and Relevance of Workshop Content', reasoning='The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. This criterion ensures the content aligns with the goal, addressing key areas like liability, authority, and fiduciary duties specifically tied to AI agents and transactions.', rating=9), SuccessCriteria(criteria='Effectiveness of Scenario Utilization', reasoning='A well-crafted scenario is crucial for participants to visualize and apply the discussed principles in a practical context. This helps bridge theoretical knowledge with real-world applications, making the workshop more impactful and providing a tangible framework for brainstorming relevant contractual provisions.', rating=8), SuccessCriteria(criteria='Comprehensiveness of Brainstorming Outcomes', reasoning="The depth and breadth of the contractual provisions identified during brainstorming sessions indicate the participants' grasp of the material. Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", rating=8)]" } }, { "stage": "criteria_evaluation_complete", "timestamp": "2025-03-22T22:07:10.151900", "inputs": { "input": "Goal: I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.\n\nSearch Results:\nTo develop a comprehensive workshop on the legal aspects of AI agents conducting transactions on behalf of users, it's essential to address the following key areas:\n\n1. Contractual and Liability Considerations:\n\n- User (Principal): Users are generally held accountable for the actions of their AI agents, as these agents operate under the users' authority. This includes contractual obligations and potential liabilities arising from transactions executed by the AI. (lawreview.uchicago.edu)\n\n- AI Agent Provider: Providers may be liable for negligence in designing, programming, or supervising AI agents, especially if such negligence leads to harm or breaches of contract. (lawreview.uchicago.edu)\n\n- Third-Party Merchants: Merchants must ensure that transactions with AI agents are legally binding and that the AI has the necessary authority to act on behalf of the user. (bizcorplaw.com)\n\n2. Fiduciary Duties of AI Agent Providers:\n\nWhen AI agent providers act as agents for users, they may owe fiduciary duties, including loyalty and care. This entails prioritizing the user's interests, maintaining confidentiality, and avoiding conflicts of interest. (dazzagreenwood.com)\n\nWorkshop Scenario:\n\nScenario: A user employs an AI agent to purchase items from various online merchants. The AI agent is developed and managed by a provider who also serves as the user's legal agent.\n\nBrainstorming Contractual Provisions:\n\n- User and AI Agent Provider Agreement:\n - Define the scope of the AI agent's authority.\n - Establish the provider's fiduciary duties, emphasizing loyalty and care.\n - Include confidentiality clauses to protect user data.\n - Outline liability terms for errors or unauthorized actions by the AI agent.\n\n- AI Agent Provider and Third-Party Merchant Agreement:\n - Confirm the AI agent's authority to act on behalf of the user.\n - Specify the merchant's obligations to verify the AI agent's credentials.\n - Detail procedures for resolving disputes arising from AI-conducted transactions.\n\n- User and Third-Party Merchant Agreement:\n - Acknowledge the AI agent's role in facilitating transactions.\n - Set terms for transaction validation and confirmation.\n - Define liability and recourse in cases of transaction errors or fraud.\n\nBy exploring these contractual provisions, workshop participants can better understand the legal frameworks necessary to manage the complexities of AI agents in commercial transactions. \n\nCriteria:\n[\n {\n "criteria": "Clarity and Relevance of Workshop Content",\n "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.",\n "rating": 9\n },\n {\n "criteria": "Effectiveness of Scenario Utilization",\n "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.",\n "rating": 8\n },\n {\n "criteria": "Engagement and Participation Levels",\n "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.",\n "rating": 7\n },\n {\n "criteria": "Comprehensiveness of Brainstorming Outcomes",\n "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.",\n "rating": 8\n },\n {\n "criteria": "Post-Workshop Assessment Scores",\n "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.",\n "rating": 9\n }\n]" }, "outputs": { "selected_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. This criterion ensures the content aligns with the goal, addressing key areas like liability, authority, and fiduciary duties specifically tied to AI agents and transactions.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "A well-crafted scenario is crucial for participants to visualize and apply the discussed principles in a practical context. This helps bridge theoretical knowledge with real-world applications, making the workshop more impactful and providing a tangible framework for brainstorming relevant contractual provisions.", "rating": 8 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The depth and breadth of the contractual provisions identified during brainstorming sessions indicate the participants' grasp of the material. Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", "rating": 8 } ] } }, { "stage": "module_output_created", "timestamp": "2025-03-22T22:07:10.157824", "inputs": { "goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions." }, "outputs": { "module_1_output": { "goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.", "success_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. The content must be directly aligned with the key areas specified, ensuring participants leave with a precise understanding of the legal aspects involved.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "The use of a practical scenario involving a user, AI agent provider, and merchant should facilitate applied learning and brainstorming. This is critical for understanding real-world application of legal principles.", "rating": 8 }, { "criteria": "Engagement and Participation Levels", "reasoning": "Participant engagement can be assessed by the level of interaction during brainstorming sessions, indicating the workshop's success in facilitating a hands-on learning environment.", "rating": 7 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The success of the workshop could be measured by the range and depth of contractual provisions identified by participants. This will reflect the participants' understanding and ability to apply the topics discussed.", "rating": 8 }, { "criteria": "Post-Workshop Assessment Scores", "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.", "rating": 9 } ], "selected_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. This criterion ensures the content aligns with the goal, addressing key areas like liability, authority, and fiduciary duties specifically tied to AI agents and transactions.", "rating": 9 }, { "criteria": "Effectiveness of Scenario Utilization", "reasoning": "A well-crafted scenario is crucial for participants to visualize and apply the discussed principles in a practical context. This helps bridge theoretical knowledge with real-world applications, making the workshop more impactful and providing a tangible framework for brainstorming relevant contractual provisions.", "rating": 8 }, { "criteria": "Comprehensiveness of Brainstorming Outcomes", "reasoning": "The depth and breadth of the contractual provisions identified during brainstorming sessions indicate the participants' grasp of the material. Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", "rating": 8 } ] } } }, { "stage": "guardrail_start", "timestamp": "2025-03-22T22:07:10.160421", "inputs": { "module_1_output": { "goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.", "success_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. 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This will reflect the participants' understanding and ability to apply the topics discussed.", "rating": 8 }, { "criteria": "Post-Workshop Assessment Scores", "reasoning": "Conducting pre- and post-workshop assessments can help determine if participants have effectively grasped the legal concepts related to AI transactional agents, providing quantitative measurement of learning outcomes.", "rating": 9 } ], "selected_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop must cover both the contractual and fiduciary dimensions thoroughly to ensure participants understand the intricate legal relationships and liabilities. 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Achieving comprehensive outcomes reflects the workshop's success in equipping participants to apply legal concepts effectively in AI-related transactional scenarios.", "rating": 8 } ] } }, "outputs": null }, { "stage": "guardrail_complete", "timestamp": "2025-03-22T22:07:10.164748", "inputs": { "module_1_output": { "goal": "I need a plan for a workshop on legal aspects of AI agents that conduct transactions to purchase items for users. It needs to look at 1) the contractual and general liability aspects for users, agent providers, and third party merchants that actually conduct the transactions with the user's AI agents, and 2) fiduciary duties for providers of the agents to the users who are deemed principals. The plan needs a simple scenario for the user/principal, AI agent provider (who is also the legal agent of the user/principal), and the third party merchants so participants can brainstorm the types of contractual provisions the users, agent providers, and third parties would all seek to have in place for these transactions. The fiduciary relationship between the agent provider and the user will require corresponding contractual provisions.", "success_criteria": [ { "criteria": "Clarity and Relevance of Workshop Content", "reasoning": "The workshop should clearly address both contractual liabilities and fiduciary responsibilities. 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APPROACHES TO UPDATE AGENTO STREAMLIT APP FOLLOW

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Claude Opus 4:

Great! I've reviewed your manual tracing approach and the Agento project. I have several questions to help create the best plan for replacing the SDK traces with your manual tracing system:

Questions about Trace Requirements:

1. Trace Granularity & Capture Points

• Do you want to capture the same level of detail as your example (process_start, user_input, module_start, agent interactions, tool calls, etc.)?
• Should we capture traces for:
  • Each module's start/end?
  • Each agent invocation (SearchAgent, CriteriaGenerator, etc.)?
  • Each tool call (like WebSearchTool)?
  • Intermediate processing steps?
  • Error conditions?

2. Trace Storage & Organization

• Should traces be stored in the same logs/manual_traces.json file for all modules, or separate files per module?
• Do you want to maintain a single process trace across all 6 modules, or restart trace IDs for each module?
• Should the Streamlit app display these traces in the UI (perhaps in the debug sections)?

3. Integration with Existing Logging

• Should we keep the existing dual logging system (standard + verbose) alongside manual traces, or consolidate them?
• Do you want the manual traces to replace the current tracing_utils.py completely?

4. Data Capture Specifics

• What specific data should we capture at each stage? Your example captures:
  • Inputs (goals, prompts, configurations)
  • Outputs (responses, generated content)
  • Timestamps
  • Stage names
• Should we sanitize/truncate large responses in traces like we do in logs?

5. Multi-Provider Support

• Are you planning to add Claude and Gemini support to Agento soon?
• Should we design the manual tracing system to be provider-agnostic from the start?
• Do you want provider information included in each trace step?

6. Streamlit Integration

• Should trace data be accessible through the Streamlit UI?
• Do you want download buttons for trace files like we have for logs?
• Should we show real-time trace updates as modules run?

7. Performance & Error Handling

• Should trace capture be non-blocking (async) to avoid slowing down module execution?
• How should we handle trace failures (e.g., disk full, permissions issues)?
• Do you want trace rotation or size limits?

Initial Plan (Subject to Revision):

Here's my preliminary plan based on your example:

Phase 1: Create Core Manual Tracing Infrastructure

1. Create utils/manual_tracing.py with the capture_step() function
2. Design trace structure to handle multi-module workflows
3. Implement trace file management (saving, loading, rotating)

Phase 2: Module-by-Module Migration

Start with Module 1 (as proof of concept):

1. Remove OpenTelemetry imports and setup
2. Replace tracing_utils.py imports with manual tracing
3. Add capture_step() calls at key points:
   • Module start/end
   • Agent invocations
   • Tool calls
   • Output generation
4. Test and validate trace output

Then migrate Modules 2-6 following the same pattern

Phase 3: Streamlit Integration

1. Update utils/file_handlers.py to support trace file downloads
2. Add trace viewing capabilities to module pages
3. Create trace visualization/debugging tools

Phase 4: Multi-Provider Preparation

1. Design provider-agnostic trace structure
2. Create wrapper classes for different LLM providers
3. Ensure trace format supports provider comparison

Would you like me to adjust this plan based on your answers to the questions above?

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ChatGPT o3

A. Questions before we touch code

1. Granularity & naming. Are the stage names and data we captured in the sample (process_start, agent_start_*, tool_call_*, etc.) exactly what you want everywhere, or do you want to simplify / extend them (e.g., add token counts, latency, cost)?

2. Persistence format. Keep the current manual_traces.json (one growing file that is overwritten every step) plus timestamped backups? Or write one file per run (e.g., traces/{process_id}.json) so concurrent sessions don’t collide?

3. OpenTelemetry coexistence. Should we remove all OpenTelemetry and Agent-SDK processors, or leave OTLP export in place and just add the manual traces beside it?

4. Streamlit UI. Do you want any of the trace data surfaced in the dashboard (e.g., a “Trace viewer” tab), or is file-level export sufficient for now?

5. Shared utilities. Is it OK to move capture_step, the global MANUAL_TRACES dict, and helper functions into a single package module (e.g., agento_streamlit/tracing_manual.py) that every module imports?

6. Future provider hooks. For Gemini/Claude, do you want placeholder capture points added now (disabled until keys are present), or leave them for later?

Once I have your answers, I’ll refine the sequencing and deliver detailed tasks.

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B. Initial replacement plan (v 0.1)

Phase	Scope	Key actions
0 – Scaffold	repo-wide	• Create tracing_manual.py with:   – global store & start_trace(process_id)   – capture_step(stage, inputs, outputs, trace_id) (identical to sample)   – helper new_process_id().• Add unit test to ensure file writes & trace merging work.
1 – Module 1 refactor	module1.py & its Streamlit page	• Remove add_trace_processor, setup_opentelemetry, SDK-specific trace(...) context. • import tracing_manual as tm; call tm.new_process_id() at the top. • Replace every current capture_step call with tm.capture_step (just a namespace change). • Strip OpenTelemetry spans unless you keep them (see Q3).
2 – Shared hooks	utils/session_state.py & utils/tracing_utils.py	• Re-implement init_tracing() to just call tm.start_trace() and store process_id in st.session_state. • Provide a lightweight TraceLoggerHooks(logger, process_id) that mirrors the detailed hooks from the sample (agent start/end, tool start/end).
3 – Modules 2-6	module2.py … module6.py	• Repeat the pattern:   – import manual tracing utils   – create/propagate process_id   – wrap major sections with tm.capture_step. • Swap their existing logging_hooks to the new TraceLoggerHooks.
4 – Streamlit integration (optional)	UI	• If desired, add a “Trace” tab that loads the current manual_traces.json, filters by process_id, and renders an expandable tree.
5 – Clean-up & docs	repo-wide	• Delete unused SDK-tracing imports. • Update README.md with the new tracing mechanism and environment-variable expectations.

Order of work: 0 → 1 → 2 → 3. Phases 4–5 are incremental and can be done after each module is migrated.

Looking forward to your answers so I can tighten the plan.

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Gemini 2.5 Pro

Okay, I've reviewed the manual-traces.md file. This is a very clear and well-thought-out approach to manual tracing! The capture_step function and the structure of MANUAL_TRACES are straightforward and effective. The included example code (module1-opentelemetry-gm-1130.py) and the manual_traces.json output clearly demonstrate its use.

The UnifiedLLMClient idea is also quite good for abstracting provider-specific calls while maintaining consistent tracing.

Before I propose a plan, I have a few questions to ensure I'm aligned with your vision for these manual traces in the Agento Streamlit app:

My Questions:

1. Granularity of "Steps":

   • The capture_step function takes a stage, inputs, and outputs. In the context of the Agento modules (which call multiple agents and tools), what level of granularity are you aiming for with "stages"?
     • A) One capture_step call at the very beginning and end of each run_module_X function?
     • B) capture_step calls before and after each Runner.run(agent, ...) call within a module?
     • C) capture_step calls before and after tool invocations (like WebSearchTool)?
     • D) Something else? (e.g., capturing logical sub-steps within an agent's execution, if possible, or at specific decision points in the module logic).
   • The example module1-opentelemetry-gm-1130.py shows capture_step being used at a fairly granular level (e.g., search_start, search_complete, criteria_generation_start, etc.). Is this the desired level for all modules?

2. Trace ID Management:

   • The capture_step function in the example allows an optional trace_id.
   • For the Agento Streamlit app, do we want a single overarching "process_id" (or "trace_id") that spans all 6 modules for a single user workflow (from initial goal to final report)?
   • Or should each module invocation (when the user clicks "Run Module X") generate its own independent trace_id?
   • The example uses a process_id for the entire run_module_1 execution. This seems like a good approach if we want to group all activities within a single module run.

3. Data to Capture in inputs and outputs:

   • For agent calls, what specific data from the agent's input and output (e.g., prompt, model, full response, Pydantic output object, token counts, cost if available) should be included in the inputs and outputs fields of capture_step? The example captures some of this.
   • The example JSON includes the full user goal and even large search summaries. Are we comfortable with potentially large JSON objects in these fields, or should we consider summarizing/truncating some of the more verbose data for the manual trace, relying on the main module output files for full details?

4. Error Handling in Traces:

   • The example module1-opentelemetry-gm-1130.py has capture_step for "search_failed" and "module_error". Should we systematically capture errors at each potential failure point within the modules in our manual traces?

5. Integration with DetailedLoggingHooks:

   • The module1-opentelemetry-gm-1130.py example passes the process_id to DetailedLoggingHooks and calls capture_step from within the hook methods (on_start, on_end, on_tool_start, on_tool_end).
   • Is this the preferred way to integrate the manual tracing with the existing agent lifecycle, or should capture_step calls be primarily outside the hooks, directly within the run_module_X logic? (Using hooks is good for consistency across all agent calls).

6. Location and Naming of Manual Trace Files:

   • The example saves manual_traces.json to a logs/ directory within the agento-streamlit/ root.
   • For the Streamlit app, where should the manual trace JSON file(s) be stored?
     • A single, continuously appended file (like the example)?
     • A new file for each complete 6-module workflow run?
     • A new file for each individual module execution? (e.g., manual_trace_module1_runID.json)
   • This will affect how trace IDs are managed and how the Streamlit app might later display or allow downloads of these traces.

7. Impact on Streamlit UI:

   • Initially, we're focusing on replacing the backend tracing. But long-term, do you envision the Streamlit UI being able to display or interact with these manual traces? (This might influence file naming/storage). The current tracing_utils.py in the Streamlit app provides download links for raw_sdk_spans.jsonl and eval_data.csv. We'll need to adapt this for the new manual trace file(s).

8. Handling Asynchronous Nature:

   • The capture_step function is synchronous. Given that the run_module_X functions are async, we need to ensure that capture_step calls are placed correctly and don't block inappropriately (though simple file I/O for small JSON updates is usually fine). The example seems to handle this okay by calling it at logical synchronous points within the async flow.

9. Deprecation of tracing_utils.py:

   • Once this manual system is in place, will the current agento-streamlit/streamlit_app/utils/tracing_utils.py (and its associated AgentoTraceProcessor) be completely removed, or will the manual tracing live alongside it for a while? (The goal seems to be replacement).

Once I have your thoughts on these, I can draft a more precise plan.

Initial (High-Level) Plan for Replacement:

Here's a general idea, which we can refine:

Phase 1: Core Manual Tracing Implementation (Module 1 Focus)

1. Integrate capture_step:
   • Add the capture_step function and MANUAL_TRACES global dictionary (or a more robust class-based equivalent if preferred for better state management, especially with multiple concurrent users if Streamlit were to scale that way, though for now global might be fine) into a new utility file, say agento-streamlit/streamlit_app/utils/manual_tracer.py.
   • Modify the file saving logic in capture_step to save to a designated, potentially run-specific, trace file (e.g., in a manual_traces/ directory).
2. Modify module1.py:
   • Import capture_step from the new utility.
   • Generate a unique process_id (or trace_id) at the beginning of run_module_1.
   • Strategically insert capture_step calls throughout run_module_1 at key stages (e.g., module start, before/after agent calls, before/after tool calls, output creation, guardrail check, module end/error). This will mirror the granularity shown in your module1-opentelemetry-gm-1130.py example.
   • If using DetailedLoggingHooks, adapt them (or create new ones) to incorporate capture_step calls, passing the process_id.
3. Adapt Streamlit Page (2_Module_1_Criteria.py):
   • The Streamlit page will need to pass a unique run_id or process_id to run_module_1 if traces are to be per-run.
   • Modify the log/trace download section to provide a link to the new manual trace JSON file for that run.
   • Remove any UI elements related to the old SDK-based trace files (raw_sdk_spans.jsonl, eval_data.csv) for Module 1.
4. Remove Old Tracing from Module 1:
   • Comment out or remove calls to init_tracing and any usage of agent_trace_context from module1.py.
   • The AgentoTraceProcessor from tracing_utils.py will no longer be actively used by Module 1.

Phase 2: Rollout to Other Modules (Modules 2-6)

1. Iterate per Module: For each subsequent module (module2.py through module6.py):
   • Apply the same capture_step integration pattern as in Module 1.
   • Ensure process_id (or the relevant trace ID) is correctly propagated or generated.
   • Adapt the corresponding Streamlit page (pages/X_Module_Y.py) to handle the new manual trace file downloads.
2. Refine capture_step Data: Based on the specifics of each module, refine what data is captured in the inputs and outputs for each step to ensure it's meaningful and useful.

Phase 3: Finalize and Cleanup

1. Remove tracing_utils.py: Once all modules are converted, the old streamlit_app/utils/tracing_utils.py can be removed.
2. Consolidate Trace Management: Review the file naming and storage for manual traces. Ensure it's robust and manageable.
3. Documentation: Update any internal documentation regarding tracing.

Phase 4 (Optional - Future): Advanced Features

1. UI for Traces: Develop Streamlit components to visualize the manual traces.
2. Multi-Provider Integration: Implement the UnifiedLLMClient concept if you plan to use Claude, Gemini, etc., and integrate capture_step within it.

Looking forward to your answers so we can make this plan more concrete!