Your agent returned 200 OK. Was it actually right?
I've been building agentic AI systems for a while now, and the thing that finally got under my skin enough to write about is that our whole stack is really good at telling us what an agent did, and almost useless at telling us whether it was right. Observability tools give you the trace, every tool call and every token, which is great for figuring out what happened after something breaks. Evals…
I have been developing agentic AI systems for some time now, and the issue that finally pushed me to write about it is that our entire system is proficient at informing us about what an agent did, but falls short in determining whether it was accurate. Observability tools provide you with the sequence of events, every request and every token, which is advantageous for understanding what transpired after something goes awry.
Evaluations offer you a score against a test set you ran at a specific point in the past. However, in real-time production scenarios, when your agent returns a self-assured, well-structured, schema-compliant 200, none of that pipeline is verifying whether the response contained within it is truly correct. A 200 can encase a confidently incorrect answer, and your dashboard will continue to display green.
To ascertain the extent of this problem, I conducted a test using a basic, weak model and a genuine structured task where I could verify the responses. The model achieved a 69% accuracy rate while appearing correct significantly more often. Subsequently, I enveloped each output in a verification check that assessed whether the answer genuinely met the specified constraints rather than merely appearing to do so, and rerolled the ones that failed.
The outcome was a 100% accuracy rate. The aspect that continues to preoccupy me is that the model's performance did not improve; the verification process was responsible for the increase. Consequently, the question that persists is whether consistency equates to correctness. A response that adheres to the schema, is fluent, and appears well-structured can still be fundamentally incorrect, and modern agent systems lack mechanisms to detect this issue during real-time operation.
I have been contemplating the development of a runtime certification layer, a component that resides between the system logging a 200 and it passing our offline evaluations, and addresses the fundamental question that appears to be overlooked – whether this particular output, at this exact moment, is genuinely correct. If you are managing agents in a production environment, I would be genuinely interested in learning how you are addressing this issue, or whether you have simply come to terms with the green dashboard.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
