Build and Govern a Production-Ready AI Agent with Amazon Bedrock AgentCore

This hands-on workshop demonstrates how to build, deploy, and govern production-grade AI agents using Amazon Bedrock AgentCore. The tutorial covers the lifecycle from agent creation to enterprise deployment with memory, gateway controls, evaluation, observability, and security.

What you will build

• Agent Memory for persistent context
• Agent Gateway for routing, authentication, and traffic management
• Agent Evaluation for automated quality testing
• Zero Trust controls for access and auditability
• Observability for monitoring, tracing, and performance analysis

Prerequisites

• AWS account with Bedrock access
• Python 3.9+ and pip
• Basic knowledge of AI agents, AWS IAM, and conversational applications

Implementation sequence

1. Configure the foundation model and least-privilege IAM roles.
2. Implement persistent agent memory with explicit retention and deletion rules.
3. Deploy a gateway with authentication, rate limiting, and request validation.
4. Build a Streamlit interface with session management and visible tool usage.
5. Configure logs, traces, latency, error, and token-usage metrics.
6. Create evaluation datasets and human review workflows.
7. Apply encryption, private connectivity, guardrails, and audit logging.

Supply Chain use cases

• Procurement assistance and contract analysis
• Inventory and replenishment recommendations
• Logistics exception handling
• Compliance and risk monitoring

Control point

A production agent is not trustworthy because the model answers well in a demo. Trust depends on explicit tool permissions, reproducible evaluations, observability, rollback procedures, and a named owner for each operational action.