Production Agentic AI Engineering Building Autonomous LLM Agents for Real-World Deployment [#1038464]

Production Agentic AI Engineering: Building Autonomous LLM Agents for Real-World Deployment
English | September 25, 2026 | ASIN: B0HL1W9DL9 | Pages not found | PDF | 17.82 MB
Building an AI agent that works in a demo is one thing. Building one that works reliably in production—at scale, inside a real organization, and under real governance requirements—is a different engineering discipline entirely.Production Agentic AI Engineering is a practical guide to designing, building, deploying, and operating autonomous LLM agents for real-world environments. It takes a production-first approach, connecting agent architecture with memory, tools, evaluation, observability, governance, and cost control.WHAT YOU WILL LEARNDesign production-ready agent architectures and understand the four-layer agentic stackBuild prompt architectures that can be versioned, tested, evaluated, and deployed systematicallyDesign working, episodic, and semantic memory systems, including RAGBuild multi-agent workflows for delegation, shared state, coordination, and conflict resolutionEvaluate and test non-deterministic, multi-step agent behaviorUse observability, tracing, and systematic debugging to diagnose production failuresApply governance, guardrails, audit trails, and human oversight to agentic systemsManage token budgets, model routing, caching, and other approaches to production cost controlWHAT MAKES THIS BOOK DIFFERENTFour production-focused case studies covering customer support, data analysis, code review, and research synthesisDetailed examination of design decisions, failure modes, and engineering solutionsPractical production principles rather than demo-focused examplesArchitectural concepts designed to remain useful across different model providers and frameworksPython examples using the Anthropic SDKWHO THIS BOOK IS FORSoftware engineers designing and building production agent systemsTechnical architects responsible for AI deployment infrastructureEngineering managers who need a practical understanding of production agentic AIIf you are building agentic AI systems that need to work in production—not just in a notebook—this book provides a practical framework for designing, deploying, operating, and improving them.