Why teams switch
The strongest evaluation programs do not start with synthetic examples alone. They start with what actually broke in production. Foxhound helps teams connect trace data, replay, and behavior comparison so evals stay grounded in real failure modes.
Build evals from real incidents
Use replay and trace inspection to surface realistic examples that reflect how your agents actually fail in production.
Compare versions with context
Pair evaluation results with run diff so teams can explain why scores changed instead of just noticing they changed.
Move from observability to improvement
Foxhound helps teams close the loop between monitoring, debugging, and regression prevention work.
Frequently asked questions
What is AI agent evaluation?
It is the process of measuring how well an agent behaves against expected outcomes, often across quality, safety, latency, and reliability dimensions.
Why use production traces in eval workflows?
Because production traces reveal real failure modes that synthetic tests often miss, making regression coverage more relevant.
Does Foxhound replace a judge model or eval framework?
No. It complements evaluation systems by giving teams better source material, debugging context, and version comparison workflows.