LLM Agent Evaluation
We build the evaluation harness that tells you whether your Large Language Model (LLM) agent is getting better or worse — golden datasets, regression gates and metrics wired into CI, so "it feels fine" becomes a number you can trust.
- 01
Golden datasets
Representative, labelled cases drawn from your real traffic — the ground truth every future change is measured against.
- 02
Metrics that mean something
Task-appropriate scoring — accuracy, faithfulness, safety, latency and cost — instead of a single misleading number.
- 03
Regression gates in CI
Evals that run on every prompt, model or pipeline change and block a merge that makes the agent worse.
LLM evaluation, answered
Why do we need evals?
Without them, a prompt tweak or model upgrade can quietly degrade quality and you'd never know until users do. Evals turn that into a gate.
How do you build a golden dataset?
From your real inputs and expected outcomes, labelled and version-controlled, so it reflects the work your agent actually does.
Can this run in our CI?
Yes — the harness is built to run in your pipeline and block regressions, and it's yours to keep and extend.
Ship the next release without holding your breath.
Tell us what you're building — or what's breaking. We reply within one business day with a concrete plan, not a sales deck.