AI agent evaluation stacks are reactive.
They measure failures after users experience them.
The 2026 Edition of Mastering AI Agent Evaluation focuses on closing that gap with two new chapters.
Chapter 6: Simulation Environments for Agentic Systems
How to treat simulation as a first-class eval primitive:
- Generate realistic scenarios
- Test full agent trajectories
- Design personas for coverage, not demos
Chapter 7: AI Agent Evaluation in Practice
Concrete, end-to-end workflows for evaluating:
- Chat agents (drift, context erosion)
- Voice agents (audio streams, interruptions, timing failures)
Includes code you can run.
If you’re an AI PM or engineer building agents for real users and real stakes, this guide is designed for you.
📥 Download Here -> https://shorturl.at/HRemM
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