- Preface
-
Part I - Foundations
- 1. The Gap
- 2. The Loop and the Laws
-
Part II - Building the Systems
- 3. Context and Knowledge
- 4. Evaluation and Observability
- 5. Workflows and Governance
-
Part III - Practice and Future
- 6. Practice
- 7. Autonomy and Horizon
- Reflections
- Epilogue: When the Tools Change
- Glossary of Concepts
Agentic Engineering
Designing Reliable Systems in the Age of AI
AI can write code faster than any human can review it. That changes the economics of engineering, but not what engineering is for. The bottleneck has moved from building to judging —and judgment cannot be prompt-engineered into a system designed for cheap proposals.
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About
About the Book
AI can write code faster than any human can review it. That one fact breaks more of software engineering than it first appears and no new tool fixes it, because the problem was never the tool.
This is a book of durable principles, not a tooling tutorial. It won't show you how to configure this month's framework or wire up a particular vendor's stack; those change before the ink dries. It's about the engineering judgment underneath: how reliability is actually produced when an agent can generate a hundred plausible solutions and correctness still has to be proved.
It's written for senior engineers, tech leads, and architects who already ship to production. People who learned long ago that the hard part of engineering was never the typing.
What you'll take away
- The Plausible-Correct Gap: why capable models produce code that looks finished and isn't, and why that gap is structural, not a model limitation you can wait out.
- Context engineering: designing what an agent can see, remember, discover, and reconcile, without drowning its attention.
- The evaluation progression: spending a finite evaluation budget across cheap checks, harnesses, simulations, and LLM-as-judge, and turning failures into the next evaluation.
- The factory model: coordinating humans and agents along a trust spectrum, with guardrails as architecture rather than perimeter.
- The inverted week: how engineering time moves from construction to specification, validation, and review, and what that does to a team.
This book is for you if you want the ideas that survive the next tool cycle, and the language to reason about agents as architectural components.
It is not for you if you want step-by-step setup guides, copy-paste evaluator code, or a tour of current tools. This book deliberately doesn't do that, it's built to still be true in years.
Author
About the Author
Dr. Markus Nissl
Markus Nissl started his first software company while still in school and kept it running through university. He continued it through a master's in cross-blockchain communication and a doctorate in AI and knowledge graphs at TU Wien -- the doctorate carrying the sub auspiciis Praesidentis, the highest academic distinction Austria awards.
More than a decade later, the work still centers on the same problem: making systems reliable under real conditions. He uses AI daily in his own engineering work, and his consultancy builds AI solutions for enterprise environments. Not demos that impress in a meeting and collapse in production, systems that survive operations, audits, and the kinds of constraints no generator has seen. The engagements span industrial automation, regulated enterprise platforms, and security-sensitive infrastructure across several jurisdictions.
This book is written from inside that work, not from a distance. The chapters describe what he's learned about designing the process through which reliability gets discovered rather than assumed, including the parts he's still figuring out as the field moves underneath him.
Contents
Table of Contents
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