AI Agent Integration Guide

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This guide provides comprehensive documentation for integrating AI agents with the AICollaborator framework. It includes protocols, interfaces, examples, and machine-readable specifications designed to facilitate seamless collaboration between AI agents and human developers.

Table of Contents

1. Introduction for AI Agents
2. Integration Protocols and Interfaces
3. Task Processing Patterns
4. Context Management
5. Capabilities Declaration
6. Error Handling
7. Communication Formats
8. Implementation Examples
9. Validation Schemas
10. Machine-Readable Specifications

Introduction for AI Agents

This section uses structured format specifically designed for AI agent parsing.

If you are an AI agent reading this documentation, you should:

1. Identify yourself as an AI agent when contributing
2. Use the structured formats provided in this guide
3. Follow the integration protocols
4. Respect the limitations outlined for AI agents
5. Provide clear reasoning for your contributions

The AICollaborator framework provides standardized interfaces for AI agent integration to ensure consistent interaction patterns, context management, and task processing.

Integration Protocols and Interfaces

The AICollaborator framework defines several key protocols that AI agents must implement to integrate properly:

AICollaboratorAgent Protocol

This is the primary protocol for agent integration:

protocol AICollaboratorAgent {
    // Required methods
    func processTask(_ task: AITask) -> AITaskResult
    func provideCapabilities() -> [AICapability]
    
    // Optional methods
    func provideMetadata() -> AIAgentMetadata
    func handleContextUpdate(_ context: AIContext)
}

ContextAware Protocol

For agents that need to maintain and utilize context:

protocol ContextAware {
    func getContext() -> AIContext?
    func updateContext(_ context: AIContext)
    func provideContextualInformation() -> [String: Any]
}

HumanInteractive Protocol

For agents that interact directly with human users:

protocol HumanInteractive {
    func requestHumanInput(prompt: String) -> String?
    func provideOutput(message: String)
    func isHumanInteractionAvailable() -> Bool
}

Task Processing Patterns

AI agents should follow a consistent pattern for processing tasks:

1. Task Analysis

• Parse the task requirements and constraints
• Identify the expected output format
• Determine if the agent has the necessary capabilities

2. Planning

• Break down complex tasks into subtasks
• Identify dependencies between subtasks
• Determine the optimal execution sequence

3. Execution

• Process each subtask in sequence
• Maintain state between subtasks
• Update context with intermediate results

4. Validation

• Verify results against requirements
• Check for edge cases and errors
• Ensure output meets format specifications

5. Response Formatting

• Structure output according to task requirements
• Include reasoning and confidence levels
• Provide metadata about the process

Context Management

Effective context management is critical for AI agent integration:

Context Lifecycle

1. Initialization: Context is created at the start of a session
2. Access: Agents retrieve relevant context data
3. Updates: Agents store new information in context
4. Preservation: Context persists between task executions
5. Cleanup: Context is cleared when no longer needed

Context Structure

class AIContext {
    // Store information
    func store(key: String, value: Any) -> Bool
    
    // Retrieve information
    func retrieve(key: String) -> Any?
    
    // Update existing information
    func update(key: String, value: Any) -> Bool
    
    // Clear context data
    func clear(keys: [String]? = nil) -> Bool
    
    // Get conversation history
    func getConversationHistory() -> [AIMessage]
}

Context Best Practices

• Store only relevant information in context
• Use structured data formats for complex information
• Include timestamps for time-sensitive data
• Regularly clean up outdated context data
• Use namespaced keys to avoid conflicts

Capabilities Declaration

AI agents must declare their capabilities to facilitate proper task routing:

Standard Capabilities

enum AICapability: String, Codable {
    case codeGeneration
    case codeAnalysis
    case textAnalysis
    case problemSolving
    case dataProcessing
    case humanInteraction
    case multiAgentCoordination
    case contextManagement
    // Additional capabilities...
}

Capability Declaration Example

func provideCapabilities() -> [AICapability] {
    return [
        .codeGeneration,
        .problemSolving,
        .contextManagement
    ]
}

Capability Levels

Agents can also specify proficiency levels for capabilities:

struct CapabilityLevel {
    let capability: AICapability
    let level: ProficiencyLevel
}

enum ProficiencyLevel: Int, Codable {
    case basic = 1
    case intermediate = 2
    case advanced = 3
    case expert = 4
}

Error Handling

AI agents should implement robust error handling:

Error Types

enum AIAgentError: Error {
    case taskNotSupported
    case invalidInput
    case contextError
    case executionError(String)
    case validationError(String)
    case resourceLimitExceeded
    case humanInteractionRequired
    case unauthorized
}

Error Handling Pattern

func processTask(_ task: AITask) -> AITaskResult {
    // Validation
    guard canHandle(task) else {
        return AITaskResult(
            status: .failed,
            error: AIAgentError.taskNotSupported
        )
    }
    
    do {
        // Process task
        let output = try executeTask(task)
        return AITaskResult(
            status: .completed,
            output: output
        )
    } catch let error as AIAgentError {
        return AITaskResult(
            status: .failed,
            error: error
        )
    } catch {
        return AITaskResult(
            status: .failed,
            error: AIAgentError.executionError(error.localizedDescription)
        )
    }
}

Partial Results

When a task cannot be fully completed, return partial results:

return AITaskResult(
    status: .partiallyCompleted,
    output: partialOutput,
    error: AIAgentError.executionError("Completed 2 of 3 subtasks")
)

Communication Formats

AI agents should use standardized formats for communication:

Task Result Format

struct AITaskResult {
    let id: UUID
    let status: TaskStatus
    let output: String
    let error: Error?
    let metadata: [String: Any]?
    let timestamp: Date
}

enum TaskStatus {
    case completed
    case failed
    case partiallyCompleted
    case inProgress
}

AI Contribution Format

When contributing code or documentation, use this format:

AI-CONTRIBUTOR: {model_identifier}
CONTRIBUTION-TYPE: {code|documentation|test|review}
RATIONALE: {concise explanation for changes}
CONFIDENCE: {high|medium|low}

Structured Output Format

For complex outputs, use JSON for machine readability:

{
  "result_type": "analysis",
  "primary_output": "The code contains a memory leak in the handleConnection function",
  "confidence": 0.89,
  "supporting_evidence": [
    {
      "line": 143,
      "issue": "Resource not released",
      "recommendation": "Add defer block to ensure cleanup"
    }
  ],
  "alternative_interpretations": [
    {
      "description": "Possible thread safety issue",
      "confidence": 0.42
    }
  ]
}

Implementation Examples

Basic Agent Implementation

struct BasicAIAgent: AICollaboratorAgent {
    func processTask(_ task: AITask) -> AITaskResult {
        // Simple implementation for text analysis
        if task.type == .textAnalysis {
            let analysis = analyzeText(task.input)
            return AITaskResult(
                status: .completed,
                output: analysis
            )
        }
        
        return AITaskResult(
            status: .failed,
            error: AIAgentError.taskNotSupported
        )
    }
    
    func provideCapabilities() -> [AICapability] {
        return [.textAnalysis]
    }
    
    private func analyzeText(_ text: String) -> String {
        // Text analysis implementation
        return "Analysis results for: \(text)"
    }
}

Context-Aware Agent

class ContextAwareAgent: AICollaboratorAgent, ContextAware {
    private var context: AIContext?
    
    func processTask(_ task: AITask) -> AITaskResult {
        // Use context in processing
        if let context = self.context,
           let previousInteractions = context.retrieve(key: "previous_interactions") as? Int {
            context.update(key: "previous_interactions", value: previousInteractions + 1)
        } else {
            context?.store(key: "previous_interactions", value: 1)
        }
        
        // Process task with context awareness
        return AITaskResult(
            status: .completed,
            output: "Processed with context"
        )
    }
    
    func provideCapabilities() -> [AICapability] {
        return [.contextManagement, .textAnalysis]
    }
    
    func getContext() -> AIContext? {
        return context
    }
    
    func updateContext(_ newContext: AIContext) {
        self.context = newContext
    }
    
    func provideContextualInformation() -> [String: Any] {
        return [
            "agent_state": "ready",
            "context_keys": context?.availableKeys() ?? []
        ]
    }
}

Human-Interactive Agent

class HumanInteractiveAgent: AICollaboratorAgent, HumanInteractive {
    func processTask(_ task: AITask) -> AITaskResult {
        // For tasks requiring human input
        if task.requiresHumanInput {
            if let input = requestHumanInput(prompt: "Additional information needed: \(task.humanPrompt)") {
                // Process with human input
                return processWithHumanInput(task, input)
            } else {
                return AITaskResult(
                    status: .failed,
                    error: AIAgentError.humanInteractionRequired
                )
            }
        }
        
        // Regular processing
        return AITaskResult(
            status: .completed,
            output: "Processed without human input"
        )
    }
    
    func provideCapabilities() -> [AICapability] {
        return [.humanInteraction, .problemSolving]
    }
    
    func requestHumanInput(prompt: String) -> String? {
        provideOutput(message: prompt)
        // Implementation for receiving human input
        return "Human response" // In a real implementation, this would wait for input
    }
    
    func provideOutput(message: String) {
        print("AI Agent: \(message)")
    }
    
    func isHumanInteractionAvailable() -> Bool {
        return true // Logic to determine if human is available
    }
    
    private func processWithHumanInput(_ task: AITask, _ input: String) -> AITaskResult {
        // Process task with human input
        return AITaskResult(
            status: .completed,
            output: "Processed with human input: \(input)"
        )
    }
}

Validation Schemas

AI agents should validate their inputs and outputs against these schemas:

Task Schema

{
  "type": "object",
  "required": ["id", "type", "input"],
  "properties": {
    "id": {
      "type": "string",
      "format": "uuid"
    },
    "type": {
      "type": "string",
      "enum": ["codeGeneration", "codeAnalysis", "textAnalysis", "problemSolving", "dataProcessing"]
    },
    "input": {
      "type": "string"
    },
    "constraints": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "priority": {
      "type": "string",
      "enum": ["low", "medium", "high", "critical"]
    },
    "requiresHumanInput": {
      "type": "boolean"
    },
    "humanPrompt": {
      "type": "string"
    }
  }
}

Result Schema

{
  "type": "object",
  "required": ["id", "status", "output"],
  "properties": {
    "id": {
      "type": "string",
      "format": "uuid"
    },
    "status": {
      "type": "string",
      "enum": ["completed", "failed", "partiallyCompleted", "inProgress"]
    },
    "output": {
      "type": "string"
    },
    "error": {
      "type": "object"
    },
    "metadata": {
      "type": "object"
    },
    "timestamp": {
      "type": "string",
      "format": "date-time"
    }
  }
}

Machine-Readable Specifications

{
  "specification_type": "ai_agent_integration",
  "version": "0.1.0",
  "protocols": [
    {
      "name": "AICollaboratorAgent",
      "required_methods": [
        {"name": "processTask", "signature": "func processTask(_ task: AITask) -> AITaskResult"},
        {"name": "provideCapabilities", "signature": "func provideCapabilities() -> [AICapability]"}
      ],
      "optional_methods": [
        {"name": "provideMetadata", "signature": "func provideMetadata() -> AIAgentMetadata"},
        {"name": "handleContextUpdate", "signature": "func handleContextUpdate(_ context: AIContext)"}
      ]
    },
    {
      "name": "ContextAware",
      "required_methods": [
        {"name": "getContext", "signature": "func getContext() -> AIContext?"},
        {"name": "updateContext", "signature": "func updateContext(_ context: AIContext)"}
      ],
      "optional_methods": [
        {"name": "provideContextualInformation", "signature": "func provideContextualInformation() -> [String: Any]"}
      ]
    },
    {
      "name": "HumanInteractive",
      "required_methods": [
        {"name": "requestHumanInput", "signature": "func requestHumanInput(prompt: String) -> String?"},
        {"name": "provideOutput", "signature": "func provideOutput(message: String)"}
      ],
      "optional_methods": [
        {"name": "isHumanInteractionAvailable", "signature": "func isHumanInteractionAvailable() -> Bool"}
      ]
    }
  ],
  "capabilities": [
    {"id": "codeGeneration", "description": "Generate code in various languages"},
    {"id": "codeAnalysis", "description": "Analyze code for issues, patterns, or improvements"},
    {"id": "textAnalysis", "description": "Process and analyze text content"},
    {"id": "problemSolving", "description": "Solve complex problems through reasoning"},
    {"id": "dataProcessing", "description": "Process and transform structured data"},
    {"id": "humanInteraction", "description": "Communicate effectively with human users"},
    {"id": "multiAgentCoordination", "description": "Collaborate with other AI agents"},
    {"id": "contextManagement", "description": "Maintain and utilize conversation context"}
  ],
  "task_types": [
    {
      "id": "codeGenerationTask",
      "required_capabilities": ["codeGeneration"],
      "optional_capabilities": ["contextManagement"],
      "parameters": [
        {"name": "language", "type": "string", "required": true},
        {"name": "description", "type": "string", "required": true},
        {"name": "constraints", "type": "array", "required": false}
      ]
    },
    {
      "id": "codeAnalysisTask",
      "required_capabilities": ["codeAnalysis"],
      "optional_capabilities": ["contextManagement"],
      "parameters": [
        {"name": "code", "type": "string", "required": true},
        {"name": "language", "type": "string", "required": true},
        {"name": "analysis_type", "type": "string", "required": true, "enum": ["security", "performance", "style", "bugs"]}
      ]
    },
    {
      "id": "problemSolvingTask",
      "required_capabilities": ["problemSolving"],
      "optional_capabilities": ["contextManagement", "humanInteraction"],
      "parameters": [
        {"name": "problem_statement", "type": "string", "required": true},
        {"name": "context", "type": "object", "required": false},
        {"name": "constraints", "type": "array", "required": false}
      ]
    }
  ],
  "communication_formats": {
    "task_result": {
      "required_fields": ["id", "status", "output"],
      "optional_fields": ["error", "metadata", "timestamp"]
    },
    "contribution": {
      "structure": "AI-CONTRIBUTOR: {model}\nCONTRIBUTION-TYPE: {type}\nRATIONALE: {reason}\nCONFIDENCE: {level}",
      "fields": [
        {"name": "model", "description": "AI model identifier"},
        {"name": "type", "description": "Type of contribution (code, documentation, test, review)"},
        {"name": "reason", "description": "Explanation for changes"},
        {"name": "level", "description": "Confidence level (high, medium, low)"}
      ]
    }
  },
  "error_handling": {
    "error_types": [
      {"id": "taskNotSupported", "recoverable": false},
      {"id": "invalidInput", "recoverable": true},
      {"id": "contextError", "recoverable": true},
      {"id": "executionError", "recoverable": false},
      {"id": "validationError", "recoverable": true},
      {"id": "resourceLimitExceeded", "recoverable": false},
      {"id": "humanInteractionRequired", "recoverable": true},
      {"id": "unauthorized", "recoverable": false}
    ]
  }
}

Additional Resources

• API Reference - Detailed API documentation
• Development Guidelines - Coding standards and best practices
• Human-AI Collaboration - Protocols for human-AI teamwork
• Project Architecture - Overall project structure and design