Most "AI agent" tutorials stop at a clever demo. This book ends with a system you can actually run.
Building Autonomous AI Agents with Python is a practical, build-as-you-go guide to designing, hardening, and shipping agentic systems-not slideware architecture. Starting from a minimal think-act-observe loop, you grow one continuous project into a production research agent with tools, MCP, memory, hybrid RAG, planning, multi-agent teams, safety gates, evaluation, and a FastAPI deployment you can put behind real users.
You will learn to turn Python functions into reliable tools, ground answers in your own documents, coordinate specialised agents when a single loop is not enough, and defend the system with validation, approvals, idempotency, injection controls, and cost limits. Every chapter extends the same codebase, so skills compound instead of resetting.
What sets this book apart is its production bias: less theory for its own sake, more working patterns, evals, traces, and deploy paths you can reuse the day after you finish a chapter.
If you are ready to move from agent demos to agents that hold up in production, open Chapter 1 and start building.
