Agent and model evaluation

Agent evaluation scores what an agent actually did, from action completion and tool selection to hallucination and RAG adherence, so you can tell whether it behaved correctly instead of guessing from spot checks.

Sampling leaves most traffic ungraded, and the failure that reaches your customer is usually in the part nobody looked at. Because the judge is a small model rather than a frontier LLM, you can afford to score every interaction, not a lucky slice.

Yes. With Autotune you correct a handful of scores directly on the trace and the evaluator adapts to your standard, with no labeling project or prompt-engineering handoff.

An evaluation that scores reliably in testing can be promoted to a runtime check that blocks or steers the bad action before the tool fires, so there is no separate model of correctness to maintain.

Yes. Run experiments and A/B tests that compare prompts, models, and agent versions against the same evaluations, so you can see which change improved correctness.