- 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
Most AI-related software literature decays within months because it focuses on temporary prompt hacks or volatile library syntax. This book treats AI agents as what they actually are: volatile, non-deterministic architectural components that require systematic engineering discipline, containment boundaries, and rigorous verification frameworks.
Written explicitly for senior software practitioners, tech leads, and principal architects, Agentic Engineering bridges real-world production engineering challenges with timeless software systems frameworks.
What You Will Discover Inside:
- The Plausible-Correct Gap: Why capable models narrow execution gaps without closing them, and how to address the structural blind spots of automated code generators.
- Context Engineering: How to design what agents can see, remember, and discover across multi-repository workspaces without drowning the model's attention budget in noise.
- The Evaluation Progression: How to map out an engineering budget across format validators, replay harnesses, statistical simulations, and LLM-as-judge frameworks.
- The Factory Model: Modern architectural implementations of cybernetic workflow controls to coordinate humans and agents safely.
- The Inverted Week: A practical breakdown of how engineering time shifts away from manual construction upstream into explicit specification, contract validation, and deep code review.
Stop relying on vibes. Learn how to engineer an environment where reliability is systematically discovered, verified, and maintained under real-world production load.
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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