Python AI Agents

AI assistants that answer questions are everywhere. AI agents that can read a real codebase, write a patch, run the test suite, and open a pull request on their own are a different, much harder problem - and this book teaches you to build one, in Python, from the ground up. This is a hands-on Python programming book, not a prompting guide. It assumes you're comfortable writing Python and have probably used an LLM API or chat-based coding assistant before, but it does not assume any prior experience with agent frameworks. Every technique is introduced from first principles, with real, runnable code in every chapter - no pseudo-code, no placeholders. You will write a tool-use loop with no framework at all, then rebuild the same agent as a LangGraph-style graph, and again on top of a vendor agent SDK (OpenAI Agents SDK, Anthropic Agent SDK, Google ADK), so you understand every layer instead of trusting a black box. You will explore the Model Context Protocol (MCP) for building portable, reusable tool servers. From there, the book turns specifically toward what a coding agent needs beyond generic agent architecture: reading and indexing a real codebase safely, writing diffs instead of blind file rewrites, testing agent behavior deterministically with fake models and pytest, running the real test suite as ground truth rather than trusting a model's self-report, and automating git branches, commits, and GitHub pull requests. The second half of the book covers what it takes to run an agent like this somewhere real: sandboxing so code execution can't do damage, permission and human-approval gates so autonomy stays bounded, multi-agent coordination for tasks too large for one agent, full observability with tracing and replay, deployment into CI/CD, and cost and latency strategy so a production agent doesn't quietly become an unmanageable bill. The book closes with a complete capstone agent that takes a GitHub issue and returns a reviewed, tested pull request, built entirely from the pieces introduced in earlier chapters. Every chapter leaves you with working code you can adapt to your own repositories. Whether you're a backend engineer curious how coding-agent products work under the hood, a team evaluating whether to build an internal coding agent, or an engineer who already ships agents and wants to firm up testing, sandboxing, and deployment practices, this book gives you the architecture, the safeguards, and the working Python to build it yourself. No prior agent-framework experience assumed - only working Python.