AWS Bedrock Managed Agents in Practice

AI agents are moving beyond experimental chatbots and becoming production systems capable of executing tasks, using tools, maintaining context, interacting with infrastructure, and automating complex workflows. Building those systems reliably requires much more than connecting an application to a language model.
AWS Bedrock Managed Agents in Practice is a hands-on technical guide to building, deploying, and operating modern agentic AI applications using AWS Bedrock Managed Agents and the surrounding AgentCore ecosystem.
Written for AI developers, cloud engineers, software developers, DevOps professionals, and technical architects, this book takes you from the foundations of managed agent architecture to the practical components required for production deployment.
You will explore how managed agents operate, how execution environments are structured, and how AgentCore Runtime provides the infrastructure needed to run agent workloads. The book explains how sessions, context, tools, permissions, memory, and runtime resources work together to create agents capable of performing useful tasks beyond simple question answering.
You will learn how to integrate OpenAI Codex into agent-driven development workflows and examine practical approaches for building agents that can reason over development tasks, interact with tools, execute structured operations, and participate in software engineering and automation processes.
Model Context Protocol is covered as an important bridge between agents and external capabilities. You will learn how MCP can expose tools and services to an agent, how tool interfaces should be structured, and how agents can interact with APIs, development environments, internal services, databases, automation platforms, and other resources.
The book also introduces SKILL.md and reusable Agent Skills, showing how specialized instructions, workflows, scripts, references, and supporting resources can be packaged into capabilities that agents can discover and use when needed.
Production AI systems also require carefully controlled access. Dedicated sections explore AWS Identity and Access Management, permission boundaries, runtime isolation, credentials, secure tool access, least-privilege design, and practical approaches to reducing the risks created when autonomous systems interact with real infrastructure.
Memory and context management are examined from an engineering perspective. You will discover how agents can maintain useful information across interactions, how session state differs from persistent knowledge, and how to design memory strategies without overwhelming the model with unnecessary context.
Throughout the book, practical architectures and implementation examples connect these technologies into complete systems. You will progress from foundational concepts to agent runtimes capable of using tools, loading specialized skills, maintaining state, interacting with cloud resources, and supporting real-world automation.
By the end, you will understand how AgentCore Runtime, OpenAI Codex, MCP, SKILL.md, IAM, memory, tools, and AWS infrastructure can work together as the foundation of production-oriented agentic applications.
Whether you are developing coding assistants, cloud automation systems, internal developer tools, enterprise AI applications, or specialized autonomous workflows, AWS Bedrock Managed Agents in Practice provides a practical path from agent experimentation to production engineering.