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How do you measure an AI agent’s accuracy? The basics of evaluation

27 July 20262 min read

Twenty annotated reference cases, only one failed: 95%, the threshold below which review has to remain exhaustive.

In brief.
How do you know whether you can trust an AI agent that prepares answers committing the company? With a benchmark: a set of real files whose correct answers have been established by experts, on which you run the system and count. Without this set-up, nobody knows how reliable the tool is or how that reliability changes over time. Feedback from the field converges: internal pilots often plateau between 70 and 80% accuracy, a level that means everything has to be reviewed and cancels out the gain.

The principle is simple; the execution is a craft

Build a representative reference set: real files, with appendices, tables and ambiguous wording, annotated by experts. Define what you count: coverage (does the system find everything that is there), accuracy (is what it asserts correct), sourcing (does the cited reference actually support the claim). Re-run this benchmark at every change of model, prompt or connector: that is the non-regression test, without which every change is a leap in the dark.

The threshold that decides everything

Below roughly 95% accuracy, experts have to check every answer, because they do not know where the errors are hiding: the system saves typing time, not checking time, and the business case collapses. Crossing this threshold takes annotated data in volume, iterations with domain experts and a permanent evaluation infrastructure. Hence the question that separates a demonstration from a production system: “show me your benchmark and your curves over time”.

The Optivalue.ai approach

Optivalue.ai attaches a confidence score from 0 to 100 to every answer, backed by its sources, which makes review targeted rather than exhaustive: human attention concentrates on low scores and gaps.


Why 95% and not 80%?

At 80%, one answer in five is wrong and nobody knows which: review has to remain exhaustive and the gain disappears.

‍What is a non-regression test?
Re-running the same benchmark after every change to check that nothing has deteriorated.

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