PRACTICAL CASE · 10
How to Tell Whether an AI Agent Has Actually Improved Rather Than Merely Sounding More Confident
After a model change, responses may look more polished while the agent misroutes requests more often, duplicates operations, or costs more to run.
Situation
Collect real-world scenarios: a routine request, an ambiguous request, a file, a duplicate, a dangerous action, and an external service error. For each one, record the expected behavior, not just an impressive response.
What to Do
Measure routing accuracy, the duplicate rate, cost, latency, the number of manual corrections, and approval violations. Run the same test suite after every change.
Verifying the Outcome
Check: compare the two versions using identical inputs. The new version passes only if it has not made safety or key metrics worse, even if its responses have become longer.
What Usually Goes Wrong
A test without an expected outcome proves nothing. And manually reviewing a single successful conversation is no substitute for regression testing.