Enterprise AI Agents

AWS Bedrock LangGraph Python AgentOps Production

Overview

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Building enterprise-grade AI agents for internal banking operations. These aren’t chatbots — they’re autonomous systems that reason, plan, use tools, and complete complex workflows.

Architecture

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flowchart TB
    subgraph INPUT["Input Layer"]
        API[API Gateway]
        EB[EventBridge]
        SQS[SQS Queues]
    end
    
    subgraph ORCHESTRATION["Agent Orchestration"]
        SUPER[Supervisor Agent]
        DOC[Document Agent]
        KNOW[Knowledge Agent]
        COMPLY[Compliance Agent]
    end
    
    subgraph TOOLS["Tools & Resources"]
        RAG[RAG System]
        BANK[Banking APIs]
        DOCS[Document Store]
    end
    
    subgraph SAFETY["Safety & Observability"]
        GUARD[Bedrock Guardrails]
        TRACE[Agent Tracing]
        EVAL[Evaluation Pipeline]
    end
    
    INPUT --> ORCHESTRATION
    ORCHESTRATION --> TOOLS
    ORCHESTRATION --> SAFETY

Key Features

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Multi-Agent Orchestration

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• Supervisor agent coordinates specialized worker agents
• Dynamic task delegation based on query intent
• Parallel execution when tasks are independent
• State sharing between agents for complex workflows

Tool Integration

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Agents can interact with:

• Internal banking APIs (accounts, transactions, products)
• Document retrieval systems
• Knowledge bases via RAG
• External compliance databases

Memory & Context

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• Session persistence across interactions
• Context windowing for long conversations
• Compaction when approaching token limits
• User preference memory for personalization

Guardrails & Safety

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• Bedrock Guardrails for content filtering
• PII detection and redaction
• Scope enforcement — agents only access permitted data
• Audit logging for compliance

Technical Stack

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Agent Infrastructure

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Observability

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Evaluation Framework

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flowchart LR
    subgraph EVAL["Evaluation Pipeline"]
        TRAJ[Trajectory Eval]
        TOOL[Tool Use Accuracy]
        RESP[Response Quality]
        HALL[Hallucination Check]
    end
    
    AGENT[Agent Run] --> EVAL
    EVAL --> METRICS[Metrics & Alerts]
    EVAL --> IMPROVE[Model Improvements]

We evaluate agents on:

• Trajectory quality — Did the agent take sensible steps?
• Tool use accuracy — Were the right tools called with correct params?
• Response quality — Is the final answer helpful and correct?
• Hallucination rate — Does the agent make things up?
• Latency — Is the response time acceptable?

Results

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Learnings

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Key lessons from building production agents:

1. Evals are everything — Without robust evaluation, you’re flying blind
2. Guardrails early — Add safety from day one, not as an afterthought
3. Tracing is crucial — Complex agent behavior needs visibility
4. Start simple — Single agent first, multi-agent only when needed
5. Human-in-the-loop — Some decisions need human approval

Related

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• MiniClaw — Open-source agent framework
• Agent Architecture Deep Dive — Technical patterns
• Agentic Protocols — MCP, A2A, AG-UI