Agentic AI with Python

A Practical Guide to Building Intelligent AI Agents is a 65-page, hands-on engineering textbook built for Python developers, computer science and data science students, and aspiring AI engineers who want to build real agentic applications from first principles. Rather than relying on fragile prompt tricks or unverified demos, this book teaches you how AI agents work under the hood: how Large Language Models (OpenAI API, Google Gemini API) process tokens and structured JSON schemas, how Python host applications validate and execute tools safely, how Retrieval-Augmented Generation (RAG) grounds responses in cited passages, and how frameworks like LangChain and LangGraph orchestrate stateful, checkpointed workflows.

Across 20 complete chapters, you progress from writing a minimal Observe–Decide–Act–Observe loop in standard Python 3.11+ to building three end-to-end portfolio projects: an AI Research Assistant with provenance validation, a Document Q&A Agent with scanned-PDF detection, and a Controlled CSV Data Analysis Assistant powered by audited Pandas functions. Complete with 9 architectural diagrams, a 42-question technical interview preparation bank, 33 practice exercises with full step-by-step solutions, a 45-term glossary, and a 30-day learning roadmap, this guide gives you the practical software engineering discipline needed to design, evaluate, secure, and deploy modern AI agents.