Overview

Build AI agents that actually work - without being locked into any framework. Most tutorials teach you a specific library. This book teaches you the patterns. Whether you use LangChain, CrewAI, AutoGen, or raw API calls, the architecture behind reliable AI agents is the same. AI Agents Pocket Guide gives you framework-agnostic blueprints you can apply anywhere. Across 14 chapters, you will learn how to design, build, and deploy autonomous systems powered by large language models: - Tool Use - Give your agents hands. Call APIs, query databases, and execute code safely. - Memory - Short-term, long-term, and episodic memory patterns that let agents learn and recall. - RAG (Retrieval-Augmented Generation) - Ground agent responses in your own data with vector search and hybrid retrieval. - Planning & Reasoning - ReAct, chain-of-thought, tree-of-thought, and task decomposition strategies. - Multi-Agent Systems - Orchestrate teams of specialized agents with delegation, routing, and consensus patterns. - Guardrails - Input validation, output filtering, and safety layers that keep agents from going off the rails. - Evaluation - Measure agent quality with deterministic and LLM-as-judge evaluation pipelines. - Deployment - Take agents to production with observability, error handling, cost control, and scaling strategies. Every pattern comes with clear Python code examples you can adapt to your stack. No filler, no hype - just the engineering decisions that separate toy demos from production-grade agents. Who this book is for: Software engineers, ML engineers, and technical leads who want to build AI agents grounded in solid architecture - not framework lock-in. Other books in Pocket Guides for Developers (standalone, no reading order): - System Design Pocket Guide: Fundamentals - System Design Pocket Guide: Interviews - This book - AI Agents Pocket Guide - Prompt Engineering Pocket Guide - Database Playbook - LLM Observability Pocket Guide - Event-Driven Architecture Pocket Guide - RAG Pocket Guide

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