AI Agents

Every step of the agent, checked. Including the judge that scores it.

Agent traces reviewed step by step by domain trained people: the right tool, the right parameters, and whether your automated judge got it right.

Illustrative dashboard, sample data

What we do for AI Agents teams.

tracestool callsLLM judgeLangfuseLangSmithLabel Studio

Each tool call and reasoning step scored against your rubric, with the first failing step tagged.

LLM judge scores checked by a person and classified as false pass, false fail or correct.

Reviewers trained on your domain and your rubric before they score a single trace.

Versioned, human reviewed evaluation sets you can compare releases against.

Weekly patterns of failure sent to your ML team, not just pass rates.

We work inside the tools you already use: Langfuse annotation queues, LangSmith, Label Studio or your own.

trace_viewer · scheduling_agent

trace 0093 · scheduling agent

book_slot · input

{ "patient": "P2210",
  "day": "Thu" "Tue",
  "time": "10:00" }

LLM judge

pass · 0.86

Reviewer R07

fail

Caller asked for Tuesday at 00:03. Judge overruled, added to the eval set.

Interface shows illustrative sample data

10dimensions checked per call, healthcare agent

3layers: agent, judge, human

Human evaluation of a voice agent that books patient appointments, including the LLM judge scoring its calls. Client anonymised at their request.

Send a small batch and get it back done, free, usually within 24 to 48 hours. You judge the work before you pay for anything.