AI Agent Observability & Evaluation

Because "seems to work" cannot survive a change. Without evals you cannot tell whether a prompt tweak improved the system or broke an edge case, and you will not notice when a provider updates the underlying model and shifts behaviour under you. Teams without evals end up afraid to change anything, which is a worse position than having a few failing tests. The suite is what makes an agent maintainable.

A combination: deterministic checks for the parts that are objective — did it call the right tool, was the field format valid, did it stay within policy — and calibrated LLM-as-judge scoring for the subjective remainder. The critical step is calibrating the judge against human ratings on a sample, so you know its agreement rate. An uncalibrated judge produces confident numbers that mean nothing.

Yes, and it is a common engagement — often prompted by an agent that worked in a pilot and is now behaving unpredictably in production. We instrument what exists, build the golden set from your real traffic, and give you the measurement layer first. That usually reveals exactly where the underlying agent needs work, which makes any subsequent rebuild far better targeted.

LangSmith, Langfuse, Arize Phoenix, Braintrust and OpenTelemetry into your existing backend such as Datadog or Grafana. Where you already run an observability stack we integrate into it rather than adding another vendor and another bill — agent traces belong alongside the rest of your production telemetry, not in a silo only the AI team looks at.

Three to six weeks for a working harness with a meaningful golden set, CI gating and production tracing. The engineering is not the slow part — agreeing what a correct outcome is, and labelling enough real examples to be representative, is where the time goes. We run that with your domain experts because they are the only people who can define correctness.

Typically a fraction of the agent build, and it is the part that determines whether the agent survives its first year. We would rather ship a smaller agent with proper evaluation than a larger one nobody can safely change — and where budget is tight, we will recommend exactly that trade.