We help AI product and startup engineering teams diagnose and fix production agent failures across evals, memory, tool use, state, observability, and human control.
Turn an intermittent production trace into a controlled case the team can rerun and inspect.
Build the benchmark that exposes the failure, then keep it as a release gate after the fix.
Trace stale context, broken state transitions, and long-running sessions that drift from intent.
Debug tool selection, malformed arguments, retries, side effects, and partial execution across real integrations.
Instrument decisions, tool calls, state changes, and outcomes so the next failure has evidence attached.
Design approval, escalation, and takeover paths for workflows where autonomous action is not enough.
We embed with the engineering team long enough to reproduce the failure and strengthen verification around it—not just patch the visible symptom.
Start with one concrete production behavior that should not have happened.
Reduce logs, traces, state, and inputs to a case we can run deliberately.
Turn the case into an executable definition of correct behavior.
Repair the model, prompt, tool, state, or control boundary causing the failure.
Run the fix against the failing case and the surrounding behavior it could affect.
Leave the team with tests, evals, and instrumentation that catch recurrence.
Grounded, auditable, Git-backed memory for coding agents.
A flight recorder and time-travel debugger for AI agents.
A historical agent project built around inspectable state transitions and human takeover.
A CLI pipeline that turns a raw company list into sourced drafts for human approval.
Send what the agent was expected to do, what actually happened, and any logs, traces, or reproduction details you already have.
