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
Configure the foundation model and least-privilege IAM roles.
Implement persistent agent memory with explicit retention and deletion rules.
Deploy a gateway with authentication, rate limiting, and request validation.
Build a Streamlit interface with session management and visible tool usage.
Configure logs, traces, latency, error, and token-usage metrics.
Create evaluation datasets and human review workflows.
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.
