Python for AI Agents

Stop Writing Scripts. Start Engineering Intelligence.
The simple chatbot era is over. The future of AI belongs to autonomous agents-systems that can perceive, reason, plan, and act to solve complex problems.
But there is a massive gap between a cool demo and a production-grade system. Most AI tutorials stop at "magic prompts" and simple API calls. They don't tell you what happens when the model hallucinates, gets stuck in an infinite loop, or outputs broken JSON that crashes your backend.
Python for AI Agents is the missing manual for the modern AI Engineer.
Written for backend developers, software architects, and Python practitioners, this book skips the hype and focuses on the hard engineering realities of building reliable, deterministic, and scalable agentic workflows. You won't just learn how to make an LLM generate text; you will learn how to architect systems that can reliably execute code, manage their own state, and collaborate in autonomous swarms.
Inside, you will master the "Stack" for AI Engineering:
From Prompts to Logic: Move beyond fragile string manipulation. Learn to use Pydantic to enforce strict input/output schemas, turning probabilistic LLM responses into structured, type-safe data your application can actually use.
The Agentic Loop: Build the ReAct (Reason + Act) pattern from scratch. Understand the cognitive architecture required for an agent to plan a task, execute tools, observe the results, and correct its own errors.
Graph-Based Orchestration: Stop building linear chains that break easily. Master LangGraph to design stateful, cyclic workflows where agents can loop, retry, and handle complex logic branches.
Tool Use & Function Calling: Teach your agents to use the "hands" of your code. Build safe, secure tool registries that allow LLMs to query databases, call APIs, and execute Python functions.
Autonomous Multi-Agent Systems: Go beyond the single agent. Architect Supervisor and Hierarchical patterns where a manager agent delegates tasks to specialized worker agents (coders, researchers, reviewers) to solve complex goals.
Production Readiness: This isn't just theory. Learn the vital "boring" stuff that keeps systems alive: unit testing probabilistic code, managing long-term memory with Vector DBs (Chroma/Qdrant), tracing execution with observability tools, and preventing wallet-draining loops.
Whether you are building a smart customer support bot, an automated data analyst, or a full-scale coding assistant, this book provides the architectural blueprints you need.
Stop relying on luck. Start building with rigor, engineer the future of autonomous systems.