{"product_id":"ai-agent-development-with-python-cameron-mclucas-9798190183436","title":"AI Agent Development with Python: Build, Evaluate, and Deploy Autonomous Agents with RAG, Memory, Tool Calling, MCP, and Multi-Agent Systems","description":"\u003cb\u003eAI Agent Development with Python: Build, Evaluate, and Deploy Autonomous Agents with RAG, Memory, Tool Calling, MCP, and Multi-Agent Systems\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eProblem\u003cp\u003e\u003cb\u003eBuilding an AI agent that produces an impressive demo is easy. Building one that can operate reliably in production is not.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eYou may already know Python, APIs, or large language models, yet still struggle to turn a basic agent loop into a dependable application. Tool calls can fail or repeat. Retrieved information may be incomplete. Memory can become stale or leak across users. Long-running tasks may lose state, while multi-agent workflows introduce unclear permissions and failure boundaries.\u003c\/p\u003e\u003cp\u003eWithout the right engineering controls, an autonomous agent can appear capable while remaining difficult to test, secure, monitor, and deploy.\u003c\/p\u003eSolution\u003cp\u003e\u003ci\u003eAI Agent Development with Python\u003c\/i\u003e provides a practical, implementation-focused path from experimental agents to controlled production systems.\u003c\/p\u003e\u003cp\u003eThrough the evolving \u003cb\u003eAutonomous Service Operations Platform\u003c\/b\u003e, you will build an agentic application one layer at a time. The book begins with a reproducible Python environment and a plain-Python agent loop, then adds structured decisions, reliable tool calling, evidence-aware retrieval-augmented generation, governed memory, durable state, MCP integrations, specialist agents, evaluation, security, and production operations.\u003c\/p\u003e\u003cp\u003eYou will learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eBuild provider-neutral agent loops with typed decisions, state transitions, execution limits, and clear completion rules.\u003c\/li\u003e\n\u003cli\u003eCreate secure, idempotent tools with authorization checks, approval gates, structured results, and failure handling.\u003c\/li\u003e\n\u003cli\u003eImplement hybrid RAG with metadata filtering, reranking, permission-aware retrieval, and safeguards against unsupported actions.\u003c\/li\u003e\n\u003cli\u003eManage working memory, long-term memory, checkpoints, context compaction, queues, retries, and interrupted runs.\u003c\/li\u003e\n\u003cli\u003eDesign MCP and multi-agent systems with restricted permissions, structured handoffs, A2A communication, and measurable failure boundaries.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe guidance is written for Python developers, backend engineers, AI application builders, technical founders, and software teams moving beyond chatbots and prototypes.\u003c\/p\u003eProof\u003cp\u003eThe book is organized around the \u003cb\u003eAGENTOPS engineering framework\u003c\/b\u003e: aim the agent, ground decisions, equip it with controlled tools, normalize state and memory, test outcomes and trajectories, observe every run, protect identity and authority, and ship improvements safely.\u003c\/p\u003e\u003cp\u003eRather than evaluating only final responses, you will measure complete agent trajectories-including retrieval quality, tool selection, arguments, approvals, latency, cost, grounding, and task completion. Reusable code patterns, evaluation templates, incident runbooks, deployment checklists, rollback guidance, and production-readiness controls support practical implementation.\u003c\/p\u003e\u003cp\u003eBy completing the book, you will understand how to build Python AI agents whose authority, evidence, memory, actions, costs, and failures remain visible and controlled. That capability matters whether you are developing customer-support automation, IT operations tools, research systems, document workflows, or internal business applications.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Cameron McLucas\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798190183436\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 08\/01\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 274\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.06lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.58d","brand":"Cameron McLucas","offers":[{"title":"Paperback","offer_id":50666068869375,"sku":"9798190183436","price":26.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/ai-agent-development-with-python-cameron-mclucas-9798190183436","provider":"WR Book House","version":"1.0","type":"link"}