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AI Agents Pocket Guide: Patterns for Building Autonomous Systems with LLMs
Paperback
Series: Pocket Guides for Developers
Publisher: Independently Published
Published: Apr 26 2026
Pages: 196
Weight: 0.59
Height: 0.42 Width: 6.00 Depth: 9.00
Language: English
- 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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