AI Agent Evaluation
Benchmarking and Testing Agent Quality for Product Leaders
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Beschreibung
AI agents can impress in a demonstration and still fail when users, tools, costs, and policies collide. Product leaders need a practical way to decide what good means, what evidence a release must produce, and when an agent should be held back.
AI Agent Evaluation is written for product managers, founders, educators, and technical leaders, including readers without a computer science background. It explains each idea in plain language and moves from product decision to measurement method. Readers learn todefine an Agent Contract, evaluate behavior, capability, reliability, and safety, and track cost and latency alongside quality.
The book shows readers how to apply Evaluation-Driven Development. Teams define acceptance criteria before changing an agent, compare every version with a trusted baseline, and turn production failures into repeatable tests. The book covers rubrics, deterministicchecks, LLM-based judges, benchmark design, regression gates, stress testing, human review, observability, authorization, prompt and context governance, and continuous improvement.
Four recurring examples in sales, healthcare, booking, and coaching show how the same evaluation discipline travels across domains and technology stacks. Practical checklists, worksheets, templates, and guided AI-assistant workflows help readers apply the methods to their own products. The result is a practical operating discipline for making AI agent quality measurable, auditable, and actionable.
- Helps product leaders turn AI agent promises into measurable release decisions
- Shows teams how to apply Evaluation-Driven Development to each change against a trusted baseline
- Explains agent quality in plain language for readers without a computer science background
- Covers behavior, capability, reliability, safety, cost, and latency in one evaluation discipline
- Provides practical contracts, rubrics, benchmarks, checklists, worksheets, and guided workflows
AI agents can impress in a demonstration and still fail when users, tools, costs, and policies collide. Product leaders need a practical way to decide what good means, what evidence a release must produce, and when an agent should be held back.
AI Agent Evaluation is written for product managers, founders, educators, and technical leaders, including readers without a computer science background. It explains each idea in plain language and moves from product decision to measurement method. Readers learn todefine an Agent Contract, evaluate behavior, capability, reliability, and safety, and track cost and latency alongside quality.
The book shows readers how to apply Evaluation-Driven Development. Teams define acceptance criteria before changing an agent, compare every version with a trusted baseline, and turn production failures into repeatable tests. The book covers rubrics, deterministicchecks, LLM-based judges, benchmark design, regression gates, stress testing, human review, observability, authorization, prompt and context governance, and continuous improvement.
Four recurring examples in sales, healthcare, booking, and coaching show how the same evaluation discipline travels across domains and technology stacks. Practical checklists, worksheets, templates, and guided AI-assistant workflows help readers apply the methods to their own products. The result is a practical operating discipline for making AI agent quality measurable, auditable, and actionable.
Helps product leaders turn AI agent promises into measurable release decisions Shows teams how to apply Evaluation-Driven Development to each change against a trusted baseline Explains agent quality in plain language for readers without a computer science background Covers behavior, capability, reliability, safety, cost, and latency in one evaluation discipline
Autor*in
Abdullah Mansoor
Themen in »AI Agent Evaluation«
AI agent evaluation reinforcement learning AI quality AI agent reliability AI metrics