A representative test set
Normal work, difficult cases, failure conditions and permission boundaries.
Agent Evals
It worked in the demo. Will it work on Monday?
Turn “looks good” into repeatable evidence of agent performance.
How it fits together
- Real scenarios
- Repeatable tests
- Release evidence
What changes.
We build evaluation around your agent’s job: representative cases, explicit success criteria and repeatable checks you can use as the agent changes.
Yours to put to work.
- Scope and acceptance criteria agreed together.
An evaluation harness
Repeatable runs, scoring rules and human review where automated judgment is insufficient.
A performance baseline
Findings, failure analysis and a regression workflow for future changes.
See an example engagement
An example scope, adapted to your environment.
- The starting point: A team cannot tell whether a new prompt improved its support agent.
- The work: Compare both versions on a reviewed set of real-world scenarios.
- The handoff: A reusable evaluation suite and an evidence-based release decision.
How we evaluate the result
- Task completion and output quality against agreed criteria.
- Failure rates by scenario, including prohibited actions.
- Regressions after a prompt, tool, model or harness change.
A useful place to start
Let’s work through your specific problem.
Start with Agent Evals, scoped to your environment.