Agent Evals & Testing
Every stalled AI initiative we've rescued had the same missing piece: nobody could say whether the system was getting better or worse. Prompts changed on vibes. Models upgraded on hope. The demo worked; production was a rumor. Evals are how you replace vibes with evidence.
We build eval harnesses the way engineers build test suites — because that's what they are. Golden datasets from your real cases, LLM-as-judge pipelines calibrated against human review, regression gates wired into CI so a prompt change that breaks accuracy never ships. It's the least glamorous part of agentic AI and the single strongest predictor of whether your system survives contact with users.
Golden datasets from real data
Curated, versioned test cases from your actual workload — not synthetic toy examples.
LLM-as-judge pipelines
Automated grading calibrated against human raters, with agreement metrics to prove it.
Regression gates in CI
Prompt and model changes blocked from shipping when scores drop.
Trajectory evals for agents
Scoring the path — tool calls, reasoning steps — not just the final answer.
Production monitoring
Live sampling and drift detection so quality degradation pages someone.
How do you evaluate outputs that are subjective?
Decompose them. "Good response" becomes concrete, checkable criteria — factual accuracy, policy compliance, tone, completeness — each scored separately. Then we calibrate automated judges against your human reviewers until agreement is high enough to trust. Subjective doesn't mean unmeasurable.
How much eval coverage do we need before launch?
Enough to cover the failure modes that would hurt you: a few hundred well-chosen cases beats ten thousand random ones. We start with your riskiest paths, wire the gate into CI, and grow the suite from real production misses.
Can evals catch problems before users do?
That's the whole point. Regression gates catch breakage pre-deploy; production sampling catches drift post-deploy. Teams running this loop find out about quality drops from a dashboard, not from an angry customer email.
Put Agent Evals & Testing to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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