The measurement layer agents are usually missing
Evaluation Suites
Test sets built from your real tasks with verified expected outcomes, scored automatically. Every prompt change, model swap and tool update runs against them before release — so improvements are proven rather than asserted.
Tracing & Replay
End-to-end traces of every run — prompts, tool calls, retries, latency and token spend at each step — with the ability to replay a failed run against a fix and confirm it is genuinely resolved.
LLM-as-Judge Pipelines
Automated scoring for outputs with no single correct answer, calibrated against human ratings so the judge is validated rather than trusted blindly. An uncalibrated judge is just a second opinion of unknown quality.
Production Monitoring
Live alerting on success rate, escalation rate, latency percentiles, error clusters and spend — because agent quality degrades from model updates and data drift, not only from your own changes.
Cost Attribution
Spend broken down by agent, tool, customer and outcome. Cost per successful outcome is the number that matters; cost per API call tells you almost nothing about whether the system is economic.
Failure Feedback Loops
Every escalation, thumbs-down and reopened case captured and triaged into the evaluation set, so the system provably improves instead of accumulating the same failure repeatedly.
From vibes to measurement
Define Success
We agree what a correct outcome actually is for your task — which is harder than it sounds and is where most eval projects quietly fail. Ambiguous success criteria produce metrics nobody trusts or acts on.
Build the Golden Set
A labelled set of real tasks with verified outcomes, covering the common path and the edge cases that actually hurt. We build this from your production traffic rather than invented examples.
Instrument the Stack
Tracing wired through every agent, tool and model call — using LangSmith, Langfuse, OpenTelemetry or your existing observability stack, rather than introducing a new vendor if you already have one that fits.
Gate the Pipeline
Evals run in CI and block releases that regress beyond your tolerance. This is the step that turns evaluation from a report someone reads occasionally into a control that actually prevents bad deploys.
Close the Loop
Production failures flow back into the golden set automatically, so coverage grows where reality proves it was thin. An eval suite that never grows stops being representative within months.
Agent Evaluation
FAQ.
Common questions about AI agent evaluation and observability — why evals matter, judging subjective tasks, retrofitting, tooling and timelines.
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