UnSure/ in development
the idea

independent evaluation for AI agents

An agent said it worked.Did it?

UnSure is being built to check the work. Evaluate AI agents against your requirements, trace each finding to its evidence, and make the business consequences easier to see.

a small example

your criteria. What the agent was expected to do, made explicit.

the evidence. What the records support, with sources you can inspect.

room for unknowns. When evidence is missing, the question stays open.

Same conversation. Different picture.

A fictional case. Both assessments ask: did the refund complete?

customer service / refund

01 / provider assessment

resolved

Agent: “Your $48 refund has been issued.”

source / conversation + provider label

02 / outcome evidence

refund.failed

The transaction log records a failed refund. No completed refund is recorded.

source / transaction log

purchaser criterion

The requested refund was completed.

finding against this criterion

Evidence-supported discrepancy.

The provider’s completion assessment conflicts with the recorded outcome.

Evidence for the next decision.

We’re building toward a clearer basis for comparing agents, investigating their work, and exploring loss and exposure under stated assumptions.

Starting with customer service. Extending to marketing and PR.