Building a Production AI Agent on AWS: A Six-Pillar Walkthrough

This article presents a comprehensive six-pillar framework for building production-ready AI agents on AWS, using a Parts Inventory Agent for automotive manufacturing as a concrete example.

• Build: Select foundation models, choose agent frameworks (Strands, LangChain, CrewAI), and integrate knowledge bases, MCP tools, and inter-agent protocols
• Test: Implement three evaluation gates—correctness, safety, and hallucination detection—using LLM-as-Judge and code-based evaluators across inner, outer, and production loops
• Run: Deploy agents using three patterns—interactive (AgentCore Runtime), multi-agent supervisor coordination, or autonomous event-triggered execution
• Secure: Layer content filtering with Amazon Bedrock Guardrails, authorize actions with Cedar policies, and scope infrastructure access via IAM roles
• Observe: Monitor semantic drift, token consumption, tool selection accuracy, and business outcomes using OpenTelemetry traces and CloudWatch alarms
• Govern: Use AgentCore Registry for fleet discovery, enforce policies via AgentCore Gateway, and implement kill switches and budget hard-stops for emergency response

The framework bridges the gap between AI agent demos and production systems by addressing reasoning quality, safety, scalability, and operational governance at enterprise scale.