Reliable AI Agents

The book presents Model Context Protocol (MCP) as a practical control plane for governed agent execution across code-first and no-code environments. Users learn how to design structured flows, manage prompt hierarchies, enforce access boundaries, measure reliability, and integrate evaluation into deployment pipelines. With architectural patterns and implementation guidance, this book gives a blueprint for building AI agents that are predictable, auditable, secure, and production ready. This book also explains how to build AI agents that behave reliably in real-world production environments. It covers the architecture of modern agentic systems, including planning, orchestration, tool execution, memory, validation, policy enforcement, security guardrails, controlled deployment, and observability. It also illustrates how to move beyond prompt-centric experimentation and engineer agents as dependable software systems. Advanced-level students focused on AI and computer science will find this book useful as a secondary textbook. Professionals working in this field, including AI engineers, software architects, platform engineers, and security professionals will also find this book useful as a reference.