Evaluating Gen AI Systems and Agents
A Practitioner's Playbook for Development Evals, Release Gates and Runtime Evidence
Evaluation as a repeatable engineering discipline across GenAI apps, RAG, coding and reasoning workflows and AI agents, from first eval suite to release gates.
- Author
- Srinivas Bommena
- Edition
- Third Edition
- Published
- May 2026
- Formats
- Kindle
- Length
- 657 pages
About this book
A GenAI system can produce an excellent answer and still fail in production. An agent can complete every step and still leave the business process in the wrong state. So what evidence is strong enough to release it, and what evidence should make you stop?
The Third Edition develops a repeatable evaluation operating model through six connected themes:
- Foundations of evaluation: from uncertainty to an evidence system. Build a first evaluation suite, understand the evaluation stack, engineer a reproducible pipeline and validate the harness itself.
- Building the evaluation assets: turn requirements, prompt behaviour and production failures into versioned evaluation contracts and governed datasets for development, regression, protected holdouts and simulation.
- Operating evaluation in production: connect observability, trace and session evidence, alerts, regression suites, release gates and human review, so production failures become permanent tests.
- Evaluating complex systems: multi-step agents, synthetic-user simulations, RAG pipelines, code generation, reasoning workflows, model migrations and fine-tuning decisions.
- Risk, trust and governance: fairness and accessibility, adversarial testing and red teaming, and governance evidence mapped to NIST AI RMF, the NIST Generative AI Profile and ISO/IEC 42001, using OWASP and MITRE ATLAS as coverage lenses.
- Implementing the evaluation system: an implementation-selection guide giving project managers the evidence to demand and developers concrete implementation contracts.
Meridian Insurance provides a continuous enterprise scenario, and eight optional browser-based companion labs cover the first evaluation suite, dataset auditing, agent run and session evaluation, RAG diagnosis, code and reasoning verification, model migration and evaluation economics.
The central question is no longer “Did the model give the right answer?” It is: what evidence would make us confident enough to let this system or agent act in real work?
