Agentic AI Reliability: Building a Testing Harness for Tool Use, Guardrails, Loops, and Workflow Outcomes (Full Book)
By Vasuki Uday Kiran Vudathala · October 16, 2026 · #Artificial Intelligence
A good final answer doesn't prove that an agentic AI workflow was correct.
This book develops a code-backed method for verifying execution and business outcomes from authoritative state, and for using that evidence to gate releases.
You'll find a runnable offline agent that demonstrates the core techniques. Chapter 19 distinguishes implemented capabilities from live-model and production extensions.
Table of Contents
- Introduction
- Part I: The Reliability Gap
- Part II: The Foundation
- Part III: The Core Verdicts
- Part IV: Behavioral Dimensions
- Part V: Cross-Cutting Concerns
- Part VI: The Decision
- Part VII: Honesty and Adoption
- Conclusion
- Glossary
- References
- Production-Readiness Checklist
Introduction
The Demo That Lied
Many agentic AI systems are initially evaluated through demos and happy-path tests. A fluent, confident response makes the system appear to work.
The risk begins when that demonstration is treated as sufficient evidence of production readiness. The agent may have skipped a required check, attempted an action it was not authorized to perform, or left state inconsistent with what its final message claimed.
In 2026, agentic systems increasingly drive real business workflows—issuing refunds, modifying records, triggering downstream jobs—so the gap between a convincing demo and verified behavior is no longer a cosmetic problem. It is a release-blocking one.
[Missing content]: The remainder of this article (from the Introduction onward) was not included in the source material. The text above is a cleaned and enhanced version of the available excerpt, with the structure preserved and minimal 2026-relevant context added. To complete the book, the missing sections for Parts I–VII, Conclusion, Glossary, References, and the Production-Readiness Checklist would need to be supplied.
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