{"product_id":"agentic-python-ashish-sharda-9798177172774","title":"Agentic Python: Build Production AI Agents from Scratch","description":"\u003cp\u003e\u003cb\u003eMost AI agent tutorials start with a framework. This book starts with the loop.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eAn agent is a model in a loop: it decides, acts through a tool, observes the result, and decides again. Most agent frameworks abstract that loop. Learn the framework first, and when something breaks - a malformed tool call, a runaway loop, unexpected cost - you may find yourself debugging an abstraction you never fully understood.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eAgentic Python\u003c\/i\u003e teaches you to build production AI agents in plain Python, calling model APIs directly over HTTP. Across 18 chapters, you build one real application: a research assistant that finds sources, reads them, cites them, and says so when it isn't sure. Each chapter adds one capability, and every code listing comes from a companion GitHub repository with a full test suite that runs offline.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eYou'll learn how to: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eRun the same agent on Anthropic or OpenAI through one interface, so switching providers is a configuration change\u003c\/li\u003e\n\u003cli\u003eGet reliable structured output with schemas, validation, and automatic repair\u003c\/li\u003e\n\u003cli\u003eBuild tools, a dispatch table, and the core agent loop from scratch\u003c\/li\u003e\n\u003cli\u003eGive your agent persistent memory with Postgres and pgvector, and search your own documents\u003c\/li\u003e\n\u003cli\u003ePlan multi-part questions, coordinate specialist agents, and require human approval before risky actions\u003c\/li\u003e\n\u003cli\u003eUse and expose tools over the Model Context Protocol (MCP), implemented by hand and interoperability-tested against the official MCP Python SDK\u003c\/li\u003e\n\u003cli\u003eAdd retries, timeouts, guardrails, tracing, and replay\u003c\/li\u003e\n\u003cli\u003eBuild a realistic evaluation dataset that checks citations against what the agent actually read, with a regression gate that runs in CI\u003c\/li\u003e\n\u003cli\u003eDefend against prompt injection, scope tool permissions, and sandbox model-generated code\u003c\/li\u003e\n\u003cli\u003eShip the system as an authenticated API with per-user budgets, structured logs, and Docker deployment\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eOnce you understand the loop, you rebuild part of the assistant in LangGraph, side by side with your own code, and test that both implementations produce the same result. Frameworks make sense once you know what they abstract.\u003c\/p\u003e\u003cp\u003eWritten for Python developers who are new to agents. No machine learning background required.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Ashish Sharda\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798177172774\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/27\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 108\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.44lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.22d","brand":"Ashish Sharda","offers":[{"title":"Paperback","offer_id":50780262465791,"sku":"9798177172774","price":24.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_6552c729-98d5-4b60-9c5f-2f9928fa6220.jpg?v=1792950348","url":"https:\/\/www.whiterainbookhouse.com\/products\/agentic-python-ashish-sharda-9798177172774","provider":"WR Book House","version":"1.0","type":"link"}