Starting with Amazon Bedrock AgentCore

Bring AI agents into production in minutes with Amazon Bedrock AgentCore

This repository contains hands-on labs demonstrating the capabilities of Amazon Bedrock AgentCore, an agentic platform to build, deploy and operate agents securely at scale - using any framework and model.

What is Amazon Bedrock AgentCore?

Amazon Bedrock AgentCore enables developers to accelerate AI agents into production with enterprise-grade scale, reliability, and security. AgentCore provides composable services that work with popular open-source frameworks and any model, eliminating the choice between open-source flexibility and enterprise requirements.

AgentCore Services Overview

Service	Purpose	Key Features
AgentCore Runtime⭐	Serverless execution	Auto-scaling, session management, container orchestration
AgentCore Identity	Credential management	API keys, OAuth tokens, secure vault
AgentCore Memory⭐	State persistence	Short-term memory, long-term storage
AgentCore Gateway	Connects agent to tools and data	Tool discovery, service integration
AgentCore Code Interpreter	Code execution	Secure sandbox, data analysis
AgentCore Browser	Web interaction	Cloud browser, auto-scaling
AgentCore Observability	Monitoring	Tracing, dashboards, debugging

Prerequisites

Before starting any lab, ensure you have:

• AWS Account with appropriate permissions
• Python 3.10+ installed
• AWS CLI configured
• Basic understanding of AI agents and AWS services

Optional: Using uv for Python Project Management

For faster Python dependency management, consider using uv instead of traditional pip and venv:

# Install dependencies with uv (faster alternative to pip)
uv pip install -r requirements.txt

# Or initialize projects with uv
uv init my-agent-project

This is optional - all labs work with standard pip commands as documented.

Overview

📓 Services	🎯 Focus & Key Learning	⏱️ Time	📊 Level
01 - Amazon Bedrock AgentCore Runtime	Serverless AI agent deployment with auto-scaling, session management, and built-in security	10 min
02 - Amazon Bedrock AgentCore Memory	Context-aware memory for conversation context and cross-session knowledge retention	10 min

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Detailed Lab Descriptions

📓 Services	🎯 Focus & Key Learning	🖼️ Diagram
Amazon Bedrock AgentCore Runtime	Focus: Serverless AI Agent Deployment Deploy production-ready AI agents with just 2 commands using AgentCore Runtime. This lab demonstrates: • Serverless agent deployment with auto-scaling • Session management and isolation • Built-in security and authentication • Integration with Strands Agents framework Key Learning: Transform prototype agents into production-ready services in minutes, not weeks.
Amazon Bedrock AgentCore Memory	Focus: Intelligent Memory Capabilities Add context-aware memory to AI agents using AgentCore Memory. This lab covers: • Short-term memory for conversation context • Long-term memory for user preferences • Cross-session knowledge retention • Personalized agent experiences Key Learning: Build agents that remember and learn from interactions to provide more intelligent responses.

Getting Started

Each lab includes:

• Prerequisites: Required setup and dependencies
• Step-by-step deployment: Automated infrastructure setup
• Code explanations: Detailed implementation walkthrough
• Cleanup instructions: Resource removal

Ready to deploy production AI agents? Start with 01-agentcore-runtime to learn the fundamentals of AgentCore Runtime.

Resources

Documentation

• What is Amazon Bedrock AgentCore?
• AgentCore Runtime How It Works
• AgentCore Memory Guide
• AgentCore Gateway Documentation
• Programmatic Agent Invocation

Code Examples

• AWS Labs AgentCore Samples

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