Definition
Agent evals
Agent evals are structured tests that measure whether an agent completed a task correctly, with isolated runs, fixed datasets, scoring rubrics, and sealed environments during the attempt.
Why it matters
You cannot trust autonomous agents in production without measuring behavior on workflows you care about.
How Islo helps
Islo provides built-in evals you can run out of the box, plus isolated computers, gateway controls, snapshots, and Harbor integration for custom benchmark suites at scale.
Evals vs traditional tests
Unit tests assert code behavior on fixed inputs. Agent evals measure whether an autonomous system completed a task end to end: did it open the right PR, fix the regression, and pass the browser flow? Evals run in isolated environments with scoring rubrics, often across dozens of task variants, so you can compare models and prompts on your own codebase instead of a public leaderboard.
When to run them
Run evals before widening agent access to a new team or workflow. They give you a baseline when someone changes the harness, swaps models, or tweaks prompts. Without that baseline, "it feels better" is the only signal — and that does not survive an incident review. Evals turn agent quality into a metric you can track over time.
Keep reading
Common questions
Does Islo include built-in evals?
Yes. You can run Islo eval suites without assembling your own harness first, and extend them with custom tasks when you need to.
How is this different from unit tests?
Evals measure end-to-end agent behavior, not just code correctness.
Can evals prevent reward hacking?
Content filters and isolated environments reduce cheating; see our Reward Hack Bench blog.