- Before You Start
- Part I: How the Work Is Organised
- 1 Two Projects
- 2 The Working Agreement
- 3 Issues, Specs and Plans
- 4 One Issue, Nine Steps
- Part II: Proving It Works
- 5 User Stories and Personas
- 6 Durable Maestro Flows
- 7 Regimes, Honest Seeds and the 3am Nightly
- 8 Simulators, Mocks and the Real Thing
- 9 Playwright and the Layers Under the UI
- 10 Review Rounds
- Part III: Shipping
- 11 The Dev and Release Split
- 12 Where CI Runs, and Where It Does Not
- 13 Environments and Migrations
- 14 Mobile Release Trains
- Part IV: Working in a Complex Domain
- 15 Vocabulary
- 16 One Authority, Evidence Cited
- 17 Ask for an Explainer
- 18 One Fact, One Home
- 19 One Goal at a Time
- 20 How a Task Gets Done
- 21 From Beta to Product
- Part V: Running Agents at Scale
- 22 Parallel Agents in One Repo
- 23 Machines Agents Can Reach
- 24 Enforce, Do Not Ask
- 25 How Agents Fail
- 26 The Failures That Wrote the Rules
- Back Matter
- A Templates
- B Checklists
- C Terms We Use
- D Guidance for AI Agents
- E Real Artifacts
- About the Author
Agentic Engineering: The Scar Table
Agents write the code now. The engineering is in everything around it.
AI agents can write code, pass tests, and still leave you with a broken product. Agentic Engineering: The Scar Table explores the real failures, hard-earned lessons, and engineering principles behind building reliable software with AI agents. Learn how to move beyond code generation toward systems you can verify, trust, and maintain.
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About
About the Book
AI coding agents can write code, run tests, fix bugs, and even implement entire features. But a passing test does not prove a working system. A successful build does not prove a usable product. And a confident agent does not guarantee a correct result.
So what does it mean to engineer software when AI agents do much of the implementation?
Agentic Engineering: The Scar Table explores this question through real engineering failures, investigations, and the practices that emerged from them.
This is not a collection of prompts or a guide to getting AI to write more code. It is a practical account of building, testing, reviewing, and maintaining software with AI agents and learning to trust the results only when there is evidence.
Each chapter examines a problem encountered in real development work: tests that passed while features were broken, unrealistic seed data that hid production bugs, agents that collided while working in parallel, and pipelines that reported success while the product remained unreachable.
Every failure left behind a rule.
Those rules form the scar table: a record of what went wrong, what was learned, and what changed afterward.
What you'll learn
- How to structure work so AI agents can execute it reliably
- How to turn user stories into verifiable tests
- Why realistic test data matters more than a green test
- How to review agent-generated changes and prevent overlapping work
- How to build guardrails around agent failures
- How to verify system behavior rather than trusting appearances
- Where human judgment and engineering ownership remain essential
Who is this book for?
This book is for software engineers, junior developers, senior engineers, technical leads, and anyone using AI coding agents to build real software.
You do not need to be an expert in agentic engineering. You need to be curious about why software fails, willing to question what appears to be working, and interested in building systems that can be trusted.
The examples are technical and practical. The lessons are earned through failures, not presented as abstract best practices.
The goal is not to make agents look reliable. The goal is to make the systems they build verifiably reliable.
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Author
About the Author
Labeeb Bappu
Labeeb Bappu is a software engineer and solution architect with over a decade of experience building and maintaining real-world software systems.
His work spans full-stack development, data architecture, distributed systems, infrastructure, and embedded device communication. He is interested in the engineering decisions behind reliable software especially the problems that emerge when systems grow beyond their original design.
In Agentic Engineering: The Scar Table, Labeeb explores the failures, lessons, and engineering principles that emerge when AI agents become part of the software development process.
He believes engineering is not just about making software work, but understanding why it works, proving that it works, and knowing where it can fail.
Contents
Table of Contents
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