An agent can pass every demo and still pick the wrong tool, make up a tool argument, or hand a user a confident answer that is wrong. None of that throws an exception or shows up in your error rate, so the first report usually comes from a customer.
We build the evaluation and observability layer that catches these failures before your users do. Deterministic checks cover the cases where the right answer is known, LLM-as-a-judge scoring covers the cases where it is not, and tool-choice and tool-correctness evals measure whether the agent picks the right tool and calls it with the right arguments. Tracing gives your subject-matter experts a view of real interactions, so their feedback reflects what the agent did instead of what someone remembers it doing.
Founder Kyle Stratis built the evaluations for AI agents at Microsoft, LogicMonitor, and Koch Industries. Every engagement ends with your team owning the harness and its documentation, and able to extend both without us.
Start with a one-week Agent Reliability Audit. You get a map of the ways your agent can fail, ranked by how likely each one is to reach a user, along with an evaluation plan, a list of what your current instrumentation cannot see, and a 90-day sequence for fixing it. The audit is a fixed fee, so there is no scoping cycle before work starts.
Set up a free call to find out whether the audit is the right place to start.
