Agent Evals & Observability
Measuring whether AI agents work: eval design, LLM-as-a-judge, test sets, tracing, observability and regression testing for agents in production.
An agent eval is only as good as its judge, and most teams never measure the judge. A grader that agrees with itself but not with users produces a green dashboard over a product that is getting worse, which is why eval design deserves the same scrutiny as the agent it scores.
These articles cover how to build evals that catch regressions: choosing between rule-based checks and LLM-as-a-judge, calibrating a judge against human labels, building test sets from real traffic, and what to trace once an agent is live.
Start here: Measuring agent memory · Maximem evals · LLM-as-a-judge (glossary)
AI Agent Evals: The Judge Is The Part Nobody Measures
An LLM judge scoring the same agent output three times will often hand back three different answers, and almost no guide to agent evaluation asks whether the judge agrees with itself or what it costs to run on everything rather than a sample.
Most Agent Eval Frameworks Are Wrong. Here's What Actually Works
Your agent is silently degrading. Move beyond static benchmarks to master AI agent evaluation. This guide explores how to design frameworks that measure reasoning, tool-use, and reliability to bridge the gap between experimental prototypes and production-ready systems.
$4K Courses Will Teach You Agent Evals. Here's a Free Guide.
Move beyond static benchmarks to master the art of AI agent evaluation. This guide explores how to design frameworks that measure reasoning, tool-use, and reliability to bridge the gap between experimental prototypes and production-ready systems.