Know it works before and after every change
Prompts and models change behaviour. We build evaluation sets, tracing and monitoring so quality is measured instead of felt.
How we approach it
Agent quality is hard to judge by eye. We create an evaluation set from real tasks, define pass criteria, and run it on every change to prompts, tools or models so regressions are caught before release.
In production, tracing records each step, tool call, token count and latency. Dashboards and alerts show cost, error rates and drift, and sampled runs are reviewed by people.
Evaluate the task, not the model
Benchmarks say little about your use case. We measure your tasks with your data.
- Model-graded and human-graded evaluation, used together.
- Regression suite in CI.
- Safe rollout with canary traffic.
Step by step
Define success
Criteria per task type.
Build the set
Real examples with expected outcomes.
Run on every change
Automated comparison against the baseline.
Monitor
Tracing, cost and drift in production.
What you receive
Evaluation harness
Repeatable runs with scores.
Tracing
Step-level visibility into every run.
Cost and latency
Per-task and per-model dashboards.
Alerts
Notification on error spikes and drift.
Common questions
How big does an evaluation set need to be?
Start with a few dozen representative cases and grow it with every failure you find in production.
Can evaluation run in CI?
Yes. It can gate releases like any other test suite.
Do you monitor after launch?
Yes. We set up tracing and dashboards, and can support ongoing tuning.
Scope your agent system with an engineer
Bring the process you want to automate. We will talk through the architecture, the risks and a realistic first release.