- 1. Introduction: How to Use This Book
- 2. Quick Start: From Zero to Orchestration
- Part I — Foundations: What Agents Actually Are
- 3. The Myth of Autonomous Agents
- 4. What We Are Actually Building
- 5. The Agent Loop: Reason, Act, Observe
- Part II — Core Building Blocks
- 6. Structured Outputs and Contracts
- 7. Tools and Function Calling
- 8. Designing the Execution Model
- 9. State Management
- 10. Memory That Works
- 11. Your First Agent, End to End
- Part III — Multi-Agent Systems
- 12. From One Agent to Many: Roles and Specialization
- 13. Orchestration Patterns
- 14. Parallel Execution and Conflict Resolution
- 15. Human-in-the-Loop Design
- Part IV — Making It Production-Grade
- 16. Reliability Engineering for Agents
- 17. Observability and Debugging
- 18. Cost Engineering
- 19. Evaluation and Testing
- 20. Security and Governance
- Part V — Advanced Patterns and Real Systems
- 21. Long-Running and Resumable Workflows
- 22. Repo-Aware Agents
- 23. The AI Project Operator
- 24. Designing Agent Systems for Teams
- Part VI — Context and Outlook
- 25. Frameworks: What They Do and When to Use Them
- 26. The Evolving Landscape
Building Agentic Systems
A Practical Guide to AI System Engineering
This book is 100% completeLast updated on 2026-07-17
Everyone has seen the demo. This book is about what comes after: an engineer's guide from promising agent prototype to a system you can trust with real work — 26 chapters through contracts, orchestration, reliability, cost, security, and the operator practice. Updated quarterly. Buy once — every update is free.
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About
About the Book
Everyone has seen the demo. This book is about what comes after.
Agents fail in ways traditional software doesn't: silently, expensively, and with great confidence. Building Agentic Systems is an engineer's guide to closing the gap between a promising prototype and a system you can trust with real work.
You'll learn:
• The agent loop, tool contracts, and structured outputs that make model behavior testable
• State, memory, and context budgets that survive long-running work
• Multi-agent orchestration without multiplying failure modes
• Reliability, cost, observability, and security — the production disciplines, adapted
• The operator practice: running AI-assisted projects day to day, with humans in the loop
A living book. The field this book covers moves quarterly, and the book moves with it. Model names, prices, and context windows are re-checked against provider documentation at every release — and on Leanpub, every update is free for as long as the book exists. You buy the book once; it stays current.
Built with the workflow it teaches. This book is written, reviewed, and maintained with the agentic system described in its own chapters: a human operator setting direction and reviewing every change, AI agents doing delegated work under version control. When the mid-2026 model lineup replaced the one the examples were first written against, parallel review agents swept all twenty-six chapters against the live API reference and a human reviewed the diff. The machinery in Part V produced the pages you're reading.
Who this book is for: software engineers who've built an agent demo and now need it to survive contact with production; tech leads deciding how (and whether) to put agents into real systems; anyone running AI-assisted projects who wants an operator's playbook, not another framework tutorial. Examples use Python and assume working programming knowledge.
Companion site: earlyadopterlabs.com/agentic-systems
Author
About the Author
Yuri Syuganov
Yuri Syuganov is a software engineer who builds and operates AI-assisted development systems — the multi-project agentic workflow described in this book runs his own work every day. He is also the author of The Modern QA Engineer Skillset, a 26-book series on software quality, and writes at modernQAcourse.com.
Contents
Table of Contents
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