E-raamat: Python AI Agents: From Prototype to Production

Python AI Agents: From Prototype to Production is a hands-on engineering guide for Python developers who want to build, deploy, and monetise production-grade autonomous AI agent systems. Written by five academics and practitioners from India with decades of combined teaching and real-world consulting experience, this book bridges the gap between knowing how to call an LLM API and actually shipping an agent that works reliably in production.The book opens where most tutorials end. Chapter one puts a working agent in your hands before any theory — a ReAct loop with live web search, running in under fifteen minutes, costing less than twenty rupees per task. From there, each chapter ships something: a market research agent, a RAG-powered document assistant, a multi-agent content pipeline, a FastAPI deployment with task queuing, and a full safety stack that has saved real production systems from expensive failures.Part one covers foundations without padding. You will understand the agent loop at the code level — not just conceptually — and learn why working memory is your most expensive resource, not your cheapest. Part two is entirely hands-on, building progressively more capable agents using LangChain, LlamaIndex, and LangGraph, with every code listing tested against pinned library versions. Part three covers multi-agent orchestration using CrewAI and AutoGen, with honest comparisons of when each framework earns its complexity. Part four covers production: safety guardrails against prompt injection, output validation using LLM-as-judge, cost controls to prevent runaway API bills, FastAPI deployment patterns, observability with LangSmith, and a complete monetisation chapter with real pricing models and unit economics calculated in Indian rupees.The book includes four bonus appendices not found in competing titles: a production deployment checklist across security, reliability, observability, cost control, and Indian regulatory compliance including the Digital Personal Data Protection Act 2023; four ready-to-use agent templates for customer support, research, document Q&A, and code review; monetisation templates with INR pricing tiers, an ROI pitch calculator for enterprise sales, and Gumroad bundle suggestions for direct sales; and a complete API reference with cost benchmarks for OpenAI and Anthropic models in rupees.Throughout, real failures are documented honestly. The chapter on safety opens with a twelve lakh rupee incident caused by missing output guardrails. The memory chapter begins with a cross-session recall failure that reached Twitter. The pricing chapter starts with a negative-margin SaaS mistake. These are not hypotheticals — they are the actual mistakes that shaped every recommendation in the book.This is the guide five Indian academics and practitioners wished existed when they started building agents professionally. It assumes you can read a Python stack trace and know what a REST API is. Everything else is explained with working code, specific costs, and the kind of direct trade-off analysis you only get from people who have debugged these systems at three in the morning.