Agent Evaluation
Measuring whether an agent actually completes real tasks, not just whether the model sounds smart.
Think of it like
A road test, not a written exam — it doesn’t matter what the driver can recite if they can’t park the car.
Example
You run the agent against 200 recorded support tickets and score how many it resolved end-to-end without a human stepping in.
How it actually works
Evaluating agents is harder than evaluating a single answer because success is a whole trajectory — many steps, tool calls, and side effects — and there’s often more than one right path. You care about task completion, cost, and safety, not just whether the final text looks good. The messy truth is that reliable agent evals are still an open problem, and a lot of reported numbers are fragile.
For product teams
A demo proves an agent can work once; evaluation tells you how often it works — insist on the second.
For engineers
Score full trajectories on task success, cost, and safety across a fixed benchmark of scenarios.
Related
- Evals — The general practice of scoring model output.
- Agent Harness — Separating model skill from scaffolding.
Read anything AI without the jargon
Look up any term in plain English, or save terms as you read with the free Chrome extension.
Open DecoderAdd to Chrome