Building Censiq: Notes From the First Version

Every organization piloting AI agents eventually asks the same question in a slightly different voice: "how do we actually know if this is working?" Usually the honest answer is: informally, through a few people's spot checks, and a vague sense of vibes.

That gap — between "we shipped an agent" and "we know how the agent performs, where it fails, and what that costs us" — is what Censiq is built to close.

Why not just use existing benchmarks

General-purpose benchmarks are useful for comparing base models. They are not built to answer "would this agent succeed at our underwriting review, our customer escalation flow, our compliance checklist." Those questions are role-specific by definition, which means the evaluation has to be role-specific too — simulated work environments, task-specific rubrics, and a comparison against how a human currently does the job.

What the first version had to get right

The first version of Censiq had to do three things well before anything else: define evaluation criteria that map to a real role rather than a generic capability, run agents against simulated work rather than static test sets, and produce output a non-technical stakeholder can act on — not just a score, but a recommendation.

Where it's headed

The current focus is deepening the evaluation lifecycle: better failure-mode capture, cleaner human-vs-agent comparison, and evidence that holds up when a deployment decision gets questioned later. More on the product itself is on the Censiq page.