Our process

Trust is not a feeling. It’s datasets, evals, and a human review process.

Connect the workflow, evaluation data, human judgment, and production feedback so every release is based on evidence.

The real problem

Most agents don’t fail because the model isn’t smart enough.

They fail because the workflow is poorly defined, context is missing or unreliable, edge cases go unhandled, and nobody agreed on what “good” meant.

Example evaluation set / 1,000 cases

Precision89.8%Recall93.4%

Predicted outcome →Actual outcome →

IssueNo issue

Selected / False negative

Missing medication context hides a contraindication, so the agent proceeds when a clinician should review the case.

Effect on systemLowers recall

Every failure should teach the system something

Reviewers focus on examples that matter. Evals protect what already works. Production tells you what to improve next.

Agent improvement protocol continuous

Selected stage / 01

Choose one valuable workflow. Map the users, constraints, risks, and what success must look like in the real world.

Success contract

JobResolve the request safely

Success≥ 92% critical-case recall

Guardrail0 unsupported actions

OutputA scoped job and measurable success criteria

The useful kind of opinionated

Architecture follows the job, the users, and the cost of being wrong.

Define quality with examples, evidence, and repeatable checks.

Human review is part of the system—not an apology for imperfect automation.

Use the simplest system that can do the job reliably and evolve with evidence.

Your engineers should understand, operate, and extend what we build.