IgorVoytyuk

"Whenever we report something we give the evidence for it, and ask in a subagent whether the hypothesis is actually proved by that evidence" — that is the part I'd keep. It makes the verdict falsifiable out loud, which is rarer than it should be.

The class I never solved sits one level below that: a check that runs, passes, and is looking at the wrong object. My top-level health signal was green for three days while zero artifacts shipped. Sixteen daemons alive, backend responding, auth token valid — every organ it polled was genuinely healthy, and nothing measured the thing leaving the building. A missing k8s connector would not have helped; the data was all there and all correct.

Related one from the same eight months: 29 quality gates, written and unit-tested and committed, none of which was ever called, because nothing was a runner. The tests proved the gates worked. Nothing proved they were wired.

Do you see that shape at customers — monitoring correct, conclusion still wrong because it describes the process instead of the result? I ask because it decides what your diagnosis agent should be sceptical of: if the input signals can be individually true and jointly meaningless, evidence-checking inside the hypothesis does not catch it.

Read-only in prod is the right constraint. The failure mode I'd most want to hear how you handle isn't a missing signal — it's a confident wrong diagnosis.

Running an autonomous pipeline for eight months, the three incidents that cost me the most days all had the surface error naming the wrong subsystem:

- "x264: malloc of size N failed / incorrect parameters" — I read it as a codec or bad-args bug and went looking there. It was RAM exhaustion. The encoder was the victim, not the cause.

- A 22x slowdown in an LLM step that was indistinguishable from a hang. It was swap: the model no longer fit in RAM, and the page file did the rest.

- A 27-minute "freeze" in a background job. The process was healthy; the pipe was buffering, so nothing appeared until exit.

In all three the logs were complete and the metrics were green. The mistake was in the inference drawn from them — and an agent will produce that wrong inference far faster than I did, with better prose attached to it.

So: does HyperProbe ever return "I don't know — here are two competing hypotheses and the cheapest check that separates them"? The discriminating check is the part I'd pay for. A single confident answer that's wrong is worse than no answer, because it sends a human down a road with the agent's credibility behind it.