Agentic AI Workflows on AWS ‐ Implementation Guide - magicplatforms/new-machine-workflows GitHub Wiki

Agentic AI Workflows on AWS - Implementation Guide

Table of Contents

  1. Legacy .NET Application Modernization Crew
  2. Autonomous FinOps Cost-Optimization Assistant
  3. Multi-Agent Data-Ops Insight Hub
  4. AWS Security Audit Crew
  5. Conversational Code-Generation Studio
  6. Cross-Framework Protocol Bridge
  7. Data-Product RAG Pipeline
  8. Tool-Integration Governance Pipeline

1. Legacy .NET Application Modernization Crew

Workflow Diagram

sequenceDiagram
    autonumber

participant User as 👤 User/System
participant EB as 🔔 EventBridge
participant DA as 🔍 Discovery Agent
participant PA as 📋 Planner Agent
participant TA as 🔄 Transformer Agent
participant QA as ✅ QA Agent
participant DevOps as 🚀 DevOps Agent
participant AWS as ☁️ AWS Services
participant Memory as 💾 Memory Store

rect rgb(230, 255, 230)
    Note over User,EB: INITIALIZATION PHASE
    User->>EB: Code push or manual modernization request
    EB->>DA: Trigger modernization workflow
end

rect rgb(255, 230, 230)
    Note over DA,AWS: DISCOVERY PHASE
    DA->>AWS: Scan repository structure
    AWS-->>DA: Return codebase metadata
    DA->>DA: Build dependency graph
    DA->>PA: Send fingerprint & dependency analysis
end

rect rgb(230, 230, 255)
    Note over PA,AWS: PLANNING PHASE
    PA->>AWS: Request Claude on Bedrock
    AWS-->>PA: AI reasoning capabilities
    PA->>PA: Analyze dependency graph
    PA->>PA: Generate per-project upgrade plan
    PA->>TA: Send modernization blueprint
end

rect rgb(255, 255, 230)
    Note over TA,AWS: TRANSFORMATION PHASE
    TA->>TA: Execute Strands Agent Loop
    loop For each project file
        TA->>AWS: Invoke Lambda for transformation
        AWS-->>TA: Return converted files
    end
    TA->>TA: Add Dockerfiles & configs
    TA->>DevOps: Push transformed branch
end

rect rgb(255, 230, 255)
    Note over DevOps,AWS: BUILD & DEPLOY PHASE
    DevOps->>AWS: Trigger CodeBuild pipeline
    AWS-->>DevOps: Build status & artifacts
    DevOps->>QA: Request quality validation
end

rect rgb(230, 255, 255)
    Note over QA,AWS: TESTING PHASE
    QA->>AWS: Run CodeCatalyst test suite
    AWS-->>QA: Test results & coverage
    alt Tests Failed
        QA->>PA: Request fixes
        PA->>TA: Generate patches
    else Tests Passed
        QA->>DevOps: Approve for deployment
    end
end

rect rgb(240, 240, 240)
    Note over DevOps,Memory: DEPLOYMENT & LEARNING PHASE
    DevOps->>AWS: Push to ECR
    DevOps->>AWS: Create ECS-Fargate task definitions
    AWS-->>DevOps: Deployment confirmation
    DevOps->>Memory: Store artifacts & traces
    Memory->>Memory: Update DynamoDB for next iteration
end

Step Descriptions

Step Component Description
1 Trigger Code push to repository or manual modernization request initiates the workflow via EventBridge rule
2 Repository Scan Discovery Agent analyzes the legacy .NET codebase, identifying all projects, dependencies, and technical debt
3 Modernization Plan Planner Agent uses Claude on Bedrock to reason over the dependency graph and create detailed upgrade strategies
4 Code Transformation Transformer Agent executes iterative transformations using Strands Agent Loop and MCP tools via Lambda
5 Automated Build DevOps Agent triggers CodeBuild pipeline on the transformed branch to validate compilation
6 Static & Unit Tests QA Agent runs comprehensive test suites through CodeCatalyst, feeding results back for fixes
7 Container Image Publish DevOps Agent builds Docker images, pushes to ECR, and creates ECS-Fargate task definitions
8 Post-mortem Memory All artifacts and execution traces are stored in DynamoDB for continuous learning

Implementation Guidelines

🎯 Key Success Factors

  1. Framework Selection: Choose the right framework (Strands, Bedrock Agents, LangGraph, CrewAI, AutoGen) based on workflow complexity and team expertise
  2. Protocol Adoption: Implement MCP for cross-framework communication to ensure interoperability
  3. Guardrail Layers: Apply proactive controls at provisioning, preventive SCPs at runtime, and detective rules for monitoring
  4. Comprehensive Instrumentation: Use LangFuse, CloudWatch, Bedrock trace, or custom OpenTelemetry pipelines for observability
  5. Continuous Iteration: Store all traces and conversations to enable prompt tuning, memory additions, and model swapping

🔒 Security Best Practices

  • Implement least-privilege IAM policies for all agents
  • Use AWS KMS for encryption at rest and in transit
  • Enable CloudTrail logging for all agent actions
  • Regular security audits using the AWS Security Audit Crew workflow
  • Implement rate limiting and cost controls for AI model usage

📈 Scaling Considerations

  • Use AWS Lambda for serverless agent execution
  • Implement caching strategies with Redis/ElastiCache
  • Design for horizontal scaling with ECS Fargate
  • Use EventBridge for event-driven architectures
  • Implement circuit breakers for external dependencies
# Agentic AI Workflows on AWS - Implementation Guide

Table of Contents

  1. [Legacy .NET Application Modernization Crew](#1-legacy-net-application-modernization-crew)
  2. [Autonomous FinOps Cost-Optimization Assistant](#2-autonomous-finops-cost-optimization-assistant)
  3. [Multi-Agent Data-Ops Insight Hub](#3-multi-agent-data-ops-insight-hub)
  4. [AWS Security Audit Crew](#4-aws-security-audit-crew)
  5. [Conversational Code-Generation Studio](#5-conversational-code-generation-studio)
  6. [Cross-Framework Protocol Bridge](#6-cross-framework-protocol-bridge)
  7. [Data-Product RAG Pipeline](#7-data-product-rag-pipeline)
  8. [Tool-Integration Governance Pipeline](#8-tool-integration-governance-pipeline)

1. Legacy .NET Application Modernization Crew

Workflow Diagram

sequenceDiagram
    autonumber
    
    participant User as 👤 User/System
    participant EB as 🔔 EventBridge
    participant DA as 🔍 Discovery Agent
    participant PA as 📋 Planner Agent
    participant TA as 🔄 Transformer Agent
    participant QA as ✅ QA Agent
    participant DevOps as 🚀 DevOps Agent
    participant AWS as ☁️ AWS Services
    participant Memory as 💾 Memory Store
    
    rect rgb(230, 255, 230)
        Note over User,EB: INITIALIZATION PHASE
        User->>EB: Code push or manual modernization request
        EB->>DA: Trigger modernization workflow
    end
    
    rect rgb(255, 230, 230)
        Note over DA,AWS: DISCOVERY PHASE
        DA->>AWS: Scan repository structure
        AWS-->>DA: Return codebase metadata
        DA->>DA: Build dependency graph
        DA->>PA: Send fingerprint & dependency analysis
    end
    
    rect rgb(230, 230, 255)
        Note over PA,AWS: PLANNING PHASE
        PA->>AWS: Request Claude on Bedrock
        AWS-->>PA: AI reasoning capabilities
        PA->>PA: Analyze dependency graph
        PA->>PA: Generate per-project upgrade plan
        PA->>TA: Send modernization blueprint
    end
    
    rect rgb(255, 255, 230)
        Note over TA,AWS: TRANSFORMATION PHASE
        TA->>TA: Execute Strands Agent Loop
        loop For each project file
            TA->>AWS: Invoke Lambda for transformation
            AWS-->>TA: Return converted files
        end
        TA->>TA: Add Dockerfiles & configs
        TA->>DevOps: Push transformed branch
    end
    
    rect rgb(255, 230, 255)
        Note over DevOps,AWS: BUILD & DEPLOY PHASE
        DevOps->>AWS: Trigger CodeBuild pipeline
        AWS-->>DevOps: Build status & artifacts
        DevOps->>QA: Request quality validation
    end
    
    rect rgb(230, 255, 255)
        Note over QA,AWS: TESTING PHASE
        QA->>AWS: Run CodeCatalyst test suite
        AWS-->>QA: Test results & coverage
        alt Tests Failed
            QA->>PA: Request fixes
            PA->>TA: Generate patches
        else Tests Passed
            QA->>DevOps: Approve for deployment
        end
    end
    
    rect rgb(240, 240, 240)
        Note over DevOps,Memory: DEPLOYMENT & LEARNING PHASE
        DevOps->>AWS: Push to ECR
        DevOps->>AWS: Create ECS-Fargate task definitions
        AWS-->>DevOps: Deployment confirmation
        DevOps->>Memory: Store artifacts & traces
        Memory->>Memory: Update DynamoDB for next iteration
    end
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Step Descriptions

Step Component Description
1 Trigger Code push to repository or manual modernization request initiates the workflow via EventBridge rule
2 Repository Scan Discovery Agent analyzes the legacy .NET codebase, identifying all projects, dependencies, and technical debt
3 Modernization Plan Planner Agent uses Claude on Bedrock to reason over the dependency graph and create detailed upgrade strategies
4 Code Transformation Transformer Agent executes iterative transformations using Strands Agent Loop and MCP tools via Lambda
5 Automated Build DevOps Agent triggers CodeBuild pipeline on the transformed branch to validate compilation
6 Static & Unit Tests QA Agent runs comprehensive test suites through CodeCatalyst, feeding results back for fixes
7 Container Image Publish DevOps Agent builds Docker images, pushes to ECR, and creates ECS-Fargate task definitions
8 Post-mortem Memory All artifacts and execution traces are stored in DynamoDB for continuous learning

2. Autonomous FinOps Cost-Optimization Assistant

Workflow Diagram

sequenceDiagram
    autonumber
    
    participant User as 👤 User
    participant UI as 🖥️ Web UI
    participant Supervisor as 👔 FinOps Supervisor
    participant Fetcher as 💰 Spend Fetcher
    participant Detector as 🔍 Anomaly Detector
    participant Writer as ✍️ Recommendation Writer
    participant Analyst as 👨‍💼 Human Analyst
    participant AWS as ☁️ AWS Services
    participant Audit as 📊 Audit System
    
    rect rgb(230, 255, 230)
        Note over User,Supervisor: QUERY INITIATION
        User->>UI: "How can I cut S3 spend in EU-West?"
        UI->>Supervisor: Forward query with context
    end
    
    rect rgb(255, 230, 230)
        Note over Supervisor,AWS: INTENT ROUTING
        Supervisor->>Supervisor: Analyze query intent
        Supervisor->>Supervisor: Select tool chain (Cost Explorer API)
        Supervisor->>Fetcher: Delegate data retrieval task
    end
    
    rect rgb(230, 230, 255)
        Note over Fetcher,AWS: DATA COLLECTION
        Fetcher->>AWS: Bedrock Action Group → Lambda
        AWS->>AWS: Call Cost Explorer API
        AWS-->>Fetcher: Return spending data
        Fetcher->>Supervisor: Provide structured cost data
    end
    
    rect rgb(255, 255, 230)
        Note over Detector,AWS: PATTERN ANALYSIS
        Supervisor->>Detector: Request anomaly analysis
        Detector->>AWS: Query Knowledge Base (S3 + OpenSearch)
        AWS-->>Detector: Historical patterns & benchmarks
        Detector->>Detector: Identify cost anomalies
        Detector->>Supervisor: Report findings
    end
    
    rect rgb(255, 230, 255)
        Note over Writer,Supervisor: RECOMMENDATION GENERATION
        Supervisor->>Writer: Request optimization plan
        Writer->>Writer: Generate human-readable recommendations
        Writer->>Writer: Calculate potential savings
        Writer->>Writer: Add account citations
        Writer->>Supervisor: Draft recommendation document
    end
    
    rect rgb(230, 255, 255)
        Note over Analyst,Supervisor: HUMAN REVIEW (OPTIONAL)
        alt Human Review Required
            Supervisor->>Analyst: Present recommendations
            Analyst->>Analyst: Review & modify
            Analyst->>Supervisor: Approve/Request changes
        else Auto-approved
            Supervisor->>Supervisor: Apply auto-approval rules
        end
    end
    
    rect rgb(240, 240, 240)
        Note over Supervisor,Audit: ACTION & AUDIT
        Supervisor->>AWS: Create Jira tickets via Action Group
        AWS-->>Supervisor: Ticket IDs
        Supervisor->>Audit: Store trace in Bedrock
        Audit->>Audit: Version and archive decision chain
        Supervisor->>UI: Return recommendations to user
        UI->>User: Display optimization plan
    end
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Step Descriptions

Step Component Description
1 User Prompt User submits cost optimization query through web interface with specific parameters
2 Intent Routing Supervisor agent analyzes intent and selects appropriate AWS Cost Explorer tools
3 Secure Data Pull Spend Fetcher uses Bedrock Action Groups with Lambda to securely retrieve cost data
4 Pattern Mining Anomaly Detector queries historical patterns from Knowledge Base to identify unusual spending
5 Draft Recommendation Recommendation Writer produces actionable, human-readable optimization plans with savings estimates
6 Human-in-the-Loop Optional analyst review for high-impact recommendations or complex scenarios
7 Ticket Creation Approved actions automatically create tracked Jira issues for implementation
8 Trace & Version Complete decision chain stored in Bedrock for audit compliance and continuous improvement

3. Multi-Agent Data-Ops Insight Hub

Workflow Diagram

sequenceDiagram
    autonumber
    
    participant Timer as ⏰ Timer
    participant Sensor as 📡 Sensor Agent
    participant VectorLoad as 📦 VectorLoad Agent
    participant RCA as 🔬 Root Cause Agent
    participant Summary as 📝 Summary Agent
    participant Engineer as 👷 Engineer
    participant Escalation as 🚨 Escalation System
    participant AWS as ☁️ AWS Services
    participant Monitor as 📊 Monitoring
    
    rect rgb(230, 255, 230)
        Note over Timer,AWS: CONTINUOUS MONITORING
        loop Every 5 minutes
            Timer->>Sensor: Trigger pipeline check
            Sensor->>AWS: Poll Glue/EMR metrics
            AWS-->>Sensor: Return pipeline status
        end
    end
    
    rect rgb(255, 230, 230)
        Note over Sensor,VectorLoad: DATA INGESTION
        Sensor->>VectorLoad: Send new log data
        VectorLoad->>VectorLoad: Chunk log entries
        VectorLoad->>AWS: Store in PGVector DB
        AWS-->>VectorLoad: Confirmation
    end
    
    rect rgb(230, 230, 255)
        Note over Sensor,RCA: ANOMALY DETECTION
        alt Anomaly Detected
            Sensor->>RCA: Send anomaly event
            RCA->>AWS: Query vector DB for similar issues
            AWS-->>RCA: Historical patterns
            RCA->>RCA: LLM correlation analysis
            RCA->>RCA: Identify root cause
        end
    end
    
    rect rgb(255, 255, 230)
        Note over RCA,Summary: INSIGHT GENERATION
        RCA->>Summary: Send analysis results
        Summary->>Summary: Generate Slack-ready synopsis
        Summary->>Summary: Format key findings
        Summary->>Engineer: Post to Slack channel
    end
    
    rect rgb(255, 230, 255)
        Note over Engineer,Monitor: FEEDBACK LOOP
        Engineer->>Engineer: React with emoji
        Engineer->>Monitor: Feedback signal
        Monitor->>Monitor: Store in LangGraph memory
        Monitor->>Monitor: Update reinforcement metrics
    end
    
    rect rgb(230, 255, 255)
        Note over Summary,Escalation: ESCALATION MANAGEMENT
        alt MTTR Target Breached
            Summary->>Escalation: Trigger escalation
            Escalation->>AWS: Create PagerDuty incident
            AWS-->>Escalation: Incident ID
            Escalation->>Engineer: Page on-call engineer
        end
    end
    
    rect rgb(240, 240, 240)
        Note over Monitor,AWS: OBSERVABILITY
        Monitor->>Monitor: Track token usage
        Monitor->>Monitor: Measure latency
        Monitor->>AWS: Push to LangSmith/LangFuse
        AWS-->>Monitor: Dashboard updates
    end
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Step Descriptions

Step Component Description
1 Pipeline Monitoring Sensor Agent polls data pipeline metrics from Glue and EMR every 5 minutes
2 Log Vectorization VectorLoad Agent chunks and ingests run logs into PGVector for semantic search
3 Anomaly Analysis Root Cause Agent receives anomaly events and correlates with historical data using LLM
4 Summary Generation Summary Agent creates concise, actionable Slack messages for engineering team
5 Feedback Collection Engineer reactions stored as reinforcement signals in LangGraph memory
6 Automated Escalation PagerDuty incidents created automatically when MTTR targets are breached
7 Meta-tool Management LangGraph workflow manages retries and implements exponential back-off strategies
8 Performance Tracking LangSmith/LangFuse monitors token usage, latency, and system performance metrics

4. AWS Security Audit Crew

Workflow Diagram

sequenceDiagram
    autonumber
    
    participant Trigger as ⏰ EventBridge
    participant Lead as 👮 Lead Auditor
    participant IAM as 🔐 IAM Reviewer
    participant Network as 🌐 Network Scanner
    participant Guard as 🛡️ GuardDuty Synthesizer
    participant Remediation as 🔧 Remediation Bot
    participant Human as 👤 Security Lead
    participant AWS as ☁️ AWS Services
    participant Report as 📄 Report System
    
    rect rgb(230, 255, 230)
        Note over Trigger,Lead: AUDIT INITIATION
        Trigger->>Lead: Daily schedule trigger
        Lead->>Lead: Receive "Audit prod accounts" directive
        Lead->>Lead: Create audit plan
    end
    
    rect rgb(255, 230, 230)
        Note over Lead,Guard: TASK DELEGATION
        Lead->>Lead: CrewAI role-based planning
        par Parallel Execution
            Lead->>IAM: Assign IAM audit task
            and
            Lead->>Network: Assign network audit task
            and
            Lead->>Guard: Assign GuardDuty review
        end
    end
    
    rect rgb(230, 230, 255)
        Note over IAM,AWS: IAM SECURITY SCAN
        IAM->>AWS: Run Access Analyzer
        AWS-->>IAM: Permission findings
        IAM->>IAM: Flag wide permissions
        IAM->>Lead: Submit IAM evidence
    end
    
    rect rgb(255, 255, 230)
        Note over Network,AWS: NETWORK AUDIT
        Network->>AWS: Audit Security Groups
        Network->>AWS: Analyze VPC Flow Logs
        AWS-->>Network: Network vulnerabilities
        Network->>Lead: Submit network findings
    end
    
    rect rgb(255, 230, 255)
        Note over Guard,AWS: THREAT SYNTHESIS
        Guard->>AWS: Query GuardDuty findings
        AWS-->>Guard: Latest security events
        Guard->>Guard: Merge JSON evidence
        Guard->>Guard: Prioritize by severity
        Guard->>Lead: Submit threat analysis
    end
    
    rect rgb(230, 255, 255)
        Note over Lead,Report: REPORT GENERATION
        Lead->>Lead: Aggregate all findings
        Lead->>Report: Generate markdown report
        Report->>AWS: Link CloudWatch metrics
        Report->>Remediation: Trigger auto-remediation
    end
    
    rect rgb(240, 240, 240)
        Note over Remediation,Human: REMEDIATION & APPROVAL
        Remediation->>Remediation: Draft SCP changes
        Remediation->>AWS: Create PRs for perimeter repo
        AWS-->>Human: Notification of pending changes
        Human->>Human: Review changes
        alt Approved
            Human->>AWS: Merge PR
        else Needs Changes
            Human->>Remediation: Comment on PR
            Remediation->>Remediation: Update patches
        end
    end
    
    rect rgb(250, 250, 250)
        Note over Trigger,AWS: CONTINUOUS LOOP
        Note right of Trigger: Repeats every 24 hours via EventBridge
    end
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Step Descriptions

Step Component Description
1 Kick-off Lead Auditor triggered by EventBridge on 24-hour schedule for production audit
2 Task Delegation CrewAI role-based planner assigns specialized scanning tasks to each agent
3 Parallel Evidence Gathering Specialist agents use MCP tools with STS-assumed roles for secure access
4 Findings Aggregation GuardDuty Synthesizer merges JSON evidence from all security sources
5 Draft Report Lead Auditor produces comprehensive markdown report with CloudWatch metric links
6 Auto-patch Generation Remediation Bot creates pull requests for Service Control Policy updates
7 Human Approval Security lead reviews and merges changes or requests modifications
8 Continuous Loop Process repeats every 24 hours via EventBridge scheduling

5. Conversational Code-Generation Studio

Workflow Diagram

sequenceDiagram
    autonumber
    
    participant PO as 👤 Product Owner
    participant Architect as 🏗️ Architect Agent
    participant Coder as 💻 Coder Agent
    participant Runner as 🏃 Runner Agent
    participant Debugger as 🐛 Debugger Agent
    participant Doc as 📚 Doc Agent
    participant AWS as ☁️ AWS Services
    participant Memory as 🧠 Memory Store
    
    rect rgb(230, 255, 230)
        Note over PO,Architect: REQUIREMENTS GATHERING
        PO->>Architect: Describe feature requirements
        Architect->>PO: Clarifying questions
        PO->>Architect: Provide specifications
    end
    
    rect rgb(255, 230, 230)
        Note over Architect,AWS: ARCHITECTURE DESIGN
        Architect->>Architect: Design module skeleton
        Architect->>Architect: Create UML diagrams in PlantUML
        Architect->>AWS: Store design artifacts
        Architect->>Coder: Send architecture blueprint
    end
    
    rect rgb(230, 230, 255)
        Note over Coder,Architect: CODE GENERATION
        Coder->>Coder: Generate initial code
        loop AutoGen Async Chat
            Coder->>Architect: Technical questions
            Architect->>Coder: Architecture guidance
            Coder->>Coder: Refine implementation
        end
        Coder->>Runner: Submit code for testing
    end
    
    rect rgb(255, 255, 230)
        Note over Runner,AWS: TEST EXECUTION
        Runner->>AWS: Deploy to Lambda/Firecracker
        AWS-->>Runner: Sandbox environment ready
        Runner->>Runner: Execute unit tests
        Runner->>Runner: Collect test results
    end
    
    rect rgb(255, 230, 255)
        Note over Runner,Debugger: DEBUGGING LOOP
        alt Tests Failed
            Runner->>Debugger: Send failure details
            Debugger->>Debugger: Analyze errors
            Debugger->>Coder: Request patches
            Coder->>Coder: Fix issues
            Coder->>Runner: Resubmit code
        else Tests Passed
            Runner->>Doc: Trigger documentation
        end
    end
    
    rect rgb(230, 255, 255)
        Note over Doc,AWS: DOCUMENTATION
        Doc->>Doc: Extract code comments
        Doc->>Doc: Generate API documentation
        Doc->>Doc: Create usage examples
        Doc->>AWS: Store documentation
    end
    
    rect rgb(240, 240, 240)
        Note over Coder,Memory: DEPLOYMENT & LEARNING
        Coder->>AWS: Push to CodeCommit
        AWS-->>Coder: Merge confirmation
        Memory->>AWS: Store all chat logs in S3
        Memory->>Memory: Prepare data for LLM fine-tuning
    end
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Step Descriptions

Step Component Description
1 User Story Intake Product Owner describes feature requirements in natural language
2 Architecture Design Architect Agent creates module skeleton and UML diagrams in PlantUML
3 Code Generation Coder Agent generates implementation with AutoGen async chat for clarifications
4 Test Execution Runner Agent executes unit tests in Lambda/Firecracker sandboxes
5 Debug Iteration Debugger Agent patches failing tests in collaboration with Coder Agent
6 Documentation Creation Doc Agent produces comprehensive API documentation from code comments
7 Code Merge Upon green tests, code is pushed to CodeCommit repository
8 Retrospective Memory All conversations stored in S3 for future LLM fine-tuning

6. Cross-Framework Protocol Bridge

Workflow Diagram

sequenceDiagram
    autonumber
    
    participant User as 👤 User
    participant Strands as 🔗 Strands Agent
    participant MCP as 🌉 MCP Protocol
    participant LangChain as ⛓️ LangChain Agent
    participant OAuth as 🔒 OAuth Provider
    participant Cache as 💾 Redis Cache
    participant AWS as ☁️ AWS Services
    participant Audit as 📊 CloudTrail
    
    rect rgb(230, 255, 230)
        Note over User,Strands: QUERY INITIATION
        User->>Strands: Submit query
        Strands->>Strands: Process in Lambda
        Strands->>Strands: Identify capability gap
    end
    
    rect rgb(255, 230, 230)
        Note over Strands,MCP: CAPABILITY DISCOVERY
        Strands->>Strands: Need Data-Insight capability
        Strands->>MCP: Request external agent connection
        MCP->>MCP: Resolve LangChain endpoint
    end
    
    rect rgb(230, 230, 255)
        Note over MCP,OAuth: AUTHENTICATION
        MCP->>OAuth: Request OAuth 2.0 token
        OAuth->>OAuth: Validate scopes
        OAuth-->>MCP: Return access token
        MCP->>MCP: Establish secure channel
    end
    
    rect rgb(255, 255, 230)
        Note over MCP,LangChain: INTER-AGENT COMMUNICATION
        MCP->>LangChain: Open HTTP stream session
        LangChain->>LangChain: Process request
        LangChain->>LangChain: Infer answer
        LangChain->>MCP: Return structured response
    end
    
    rect rgb(255, 230, 255)
        Note over Strands,User: RESPONSE SYNTHESIS
        MCP-->>Strands: Deliver external insights
        Strands->>Strands: Merge with local context
        Strands->>User: Provide unified response
    end
    
    rect rgb(230, 255, 255)
        Note over MCP,Audit: LOGGING & AUDIT
        MCP->>Audit: Log request to CloudTrail
        Audit->>AWS: Store in organization trail
        MCP->>Cache: Cache response in Redis
    end
    
    rect rgb(240, 240, 240)
        Note over Cache,Strands: PERFORMANCE OPTIMIZATION
        Note right of Cache: Future requests check cache first for low-latency responses
    end
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Step Descriptions

Step Component Description
1 User Query User submits query to Strands agent running in AWS Lambda
2 Capability Recognition Strands identifies need for specialized Data-Insight capability from external agent
3 Protocol Bridge MCP Inter-Agent protocol opens streamable HTTP session to LangChain stack
4 External Processing LangChain agent processes request and returns structured MCP message
5 Context Merge Strands agent combines external response with its own context
6 Security Layer OAuth 2.0 scopes protect both sides of the communication
7 Audit Trail All requests logged in central CloudTrail organization trail
8 Response Caching Results cached in Redis for future low-latency responses

7. Data-Product RAG Pipeline

Workflow Diagram

sequenceDiagram
    autonumber
    
    participant User as 👤 User
    participant Router as 🚦 Query Router
    participant Retriever as 🔍 Retriever
    participant Grounding as ⚓ Grounding Agent
    participant Generator as ✨ LLM Generator
    participant Compliance as ✅ Compliance Checker
    participant AWS as ☁️ AWS Services
    participant Metrics as 📊 CloudWatch
    
    rect rgb(230, 255, 230)
        Note over User,Router: QUERY RECEPTION
        User->>Router: "Summarize competitive landscape for product X"
        Router->>Router: Classify query domain
        Router->>Router: Select appropriate vector store
    end
    
    rect rgb(255, 230, 230)
        Note over Router,AWS: DOCUMENT RETRIEVAL
        Router->>Retriever: Forward classified query
        Retriever->>AWS: Query Bedrock Knowledge Base
        AWS-->>Retriever: Return top-k documents
        Retriever->>Grounding: Send retrieved documents
    end
    
    rect rgb(230, 230, 255)
        Note over Grounding,Retriever: SOURCE VALIDATION
        Grounding->>Grounding: Validate source authenticity
        Grounding->>Grounding: Check for hallucinations
        alt Invalid Sources
            Grounding->>Grounding: Drop unreliable docs
        end
        Grounding->>Generator: Pass validated sources
    end
    
    rect rgb(255, 255, 230)
        Note over Generator,Generator: CONTENT GENERATION
        Generator->>Generator: Craft comprehensive brief
        Generator->>Generator: Add source citations
        Generator->>Generator: Structure with headers
        Generator->>Compliance: Submit for review
    end
    
    rect rgb(255, 230, 255)
        Note over Compliance,AWS: COMPLIANCE CHECK
        Compliance->>AWS: Run RCAC validation
        AWS-->>Compliance: Access control results
        Compliance->>AWS: PII redaction Lambda
        AWS-->>Compliance: Sanitized content
        Compliance->>Compliance: Final approval
    end
    
    rect rgb(230, 255, 255)
        Note over Compliance,User: DELIVERY
        Compliance->>AWS: Generate signed PDF
        AWS->>AWS: Create S3 presigned URL
        AWS-->>User: Deliver secure link
        User->>User: Access document
    end
    
    rect rgb(240, 240, 240)
        Note over Metrics,AWS: PERFORMANCE TRACKING
        Metrics->>AWS: Push response SLOs
        AWS->>AWS: CloudWatch Evidently
        AWS-->>Metrics: Dashboard updates
    end
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Step Descriptions

Step Component Description
1 Prompt Reception Query Router receives and classifies user request for competitive analysis
2 Domain Routing Router selects appropriate vector store based on query classification
3 Document Retrieval Retriever queries Bedrock Knowledge Base or OpenSearch for top-k relevant documents
4 Source Grounding Grounding Agent validates sources and removes potential hallucinations
5 Brief Generation LLM Generator creates comprehensive brief with proper source citations
6 Compliance Review Compliance Checker runs RCAC and PII redaction via Lambda functions
7 Secure Delivery System generates signed PDF with S3 presigned URL for secure access
8 Metrics Collection Response-level SLOs pushed to CloudWatch Evidently for monitoring

8. Tool-Integration Governance Pipeline

Workflow Diagram

sequenceDiagram
    autonumber
    
    participant Dev as 👨‍💻 Developer
    participant Catalog as 📚 Service Catalog
    participant Scanner as 🔍 Compliance Scanner
    participant ControlTower as 🏗️ Control Tower
    participant Canary as 🐤 Synthetic Canary
    participant SecHub as 🛡️ Security Hub
    participant Lambda as ⚡ Lambda Function
    participant RAM as 🔄 Resource Access Manager
    participant Config as ⚙️ AWS Config
    
    rect rgb(230, 255, 230)
        Note over Dev,Catalog: TOOL SUBMISSION
        Dev->>Catalog: Submit MCP-tool manifest
        Catalog->>Scanner: Forward for validation
    end
    
    rect rgb(255, 230, 230)
        Note over Scanner,Scanner: STATIC COMPLIANCE
        Scanner->>Scanner: Check OAuth scopes
        Scanner->>Scanner: Validate permissions
        alt Wild-card Detected
            Scanner->>Dev: Deny submission
            Dev->>Dev: Fix permissions
        else Compliant
            Scanner->>ControlTower: Approve for sandbox
        end
    end
    
    rect rgb(230, 230, 255)
        Note over ControlTower,ControlTower: SANDBOX DEPLOYMENT
        ControlTower->>ControlTower: Spin up test account
        ControlTower->>ControlTower: Deploy tool in isolation
        ControlTower->>Canary: Trigger runtime tests
    end
    
    rect rgb(255, 255, 230)
        Note over Canary,SecHub: RUNTIME TESTING
        Canary->>Canary: Call tool endpoints
        Canary->>Canary: Verify authentication
        Canary->>Canary: Test error handling
        alt Tests Pass
            Canary->>SecHub: Create suppress flag
        else Tests Fail
            Canary->>SecHub: High-severity alert
            SecHub->>Dev: Notify failure
        end
    end
    
    rect rgb(255, 230, 255)
        Note over SecHub,Lambda: SCP UPDATE
        SecHub->>Lambda: Trigger approval workflow
        Lambda->>Lambda: Update allowed-tools SCP
        Lambda->>Lambda: Add tool ARN to whitelist
        Lambda->>RAM: Request cross-account share
    end
    
    rect rgb(230, 255, 255)
        Note over RAM,RAM: PRODUCTION SHARE
        RAM->>RAM: Configure sharing rules
        RAM->>RAM: Share with production OUs
        RAM->>Dev: Confirm deployment
    end
    
    rect rgb(240, 240, 240)
        Note over Config,Config: DRIFT DETECTION
        loop Weekly
            Config->>Config: Run conformance pack
            Config->>Config: Check compliance status
            alt Drift Detected
                Config->>SecHub: Create finding
            end
        end
    end
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Step Descriptions

Step Component Description
1 Tool Submission Developer submits MCP-tool manifest to Service Catalog for registration
2 Static Compliance Scan Proactive control checks OAuth scopes and denies wild-card permissions
3 Auto Deployment Control Tower Account Factory creates isolated sandbox for testing
4 Runtime Test Suite Synthetic Canary validates authentication, endpoints, and error handling
5 Security Hub Finding Test results create suppress flag for passed tests or high-severity alerts for failures
6 Automatic SCP Update Lambda function adds approved tool ARN to allowed-tools Service Control Policy
7 Cross-account Share Resource Access Manager shares tool with production organizational units
8 Periodic Drift-detection AWS Config conformance pack checks compliance weekly and flags violations

Implementation Guidelines

🎯 Key Success Factors

  1. Framework Selection: Choose the right framework (Strands, Bedrock Agents, LangGraph, CrewAI, AutoGen) based on workflow complexity and team expertise
  2. Protocol Adoption: Implement MCP for cross-framework communication to ensure interoperability
  3. Guardrail Layers: Apply proactive controls at provisioning, preventive SCPs at runtime, and detective rules for monitoring
  4. Comprehensive Instrumentation: Use LangFuse, CloudWatch, Bedrock trace, or custom OpenTelemetry pipelines for observability
  5. Continuous Iteration: Store all traces and conversations to enable prompt tuning, memory additions, and model swapping

🔒 Security Best Practices

  • Implement least-privilege IAM policies for all agents
  • Use AWS KMS for encryption at rest and in transit
  • Enable CloudTrail logging for all agent actions
  • Regular security audits using the AWS Security Audit Crew workflow
  • Implement rate limiting and cost controls for AI model usage

📈 Scaling Considerations

  • Use AWS Lambda for serverless agent execution
  • Implement caching strategies with Redis/ElastiCache
  • Design for horizontal scaling with ECS Fargate
  • Use EventBridge for event-driven architectures
  • Implement circuit breakers for external dependencies
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