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