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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. Run

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.

karanraina 2mo ago
yes, for example we dont have connectors for k8s yet, so we are blind to memkills triggered due to sidecars. trying to debug using our tool might even lookup some memleak candidates in your primary container, but there wont be conclusive evidence for it and it would say so. for in app errors all we do is hypothesize and either prove/disprove that using data from running system. and whenever we do report something we give have the evidence for it. its not fool proof but just asking does this hypothesis gets proved with this evidence in a subagent mostly does the trick
- IgorVoytyuk 2mo ago
  "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.
  - karanraina 2mo ago
    i just gave the k8s example to show that we wont be able to find root cause of every issue. our snapshots give evidence, whether or not its helpful is determined by the engineer/ai agent I'm not saying every issue would be diagnosed this way, sometimes, it might well be beyond your control like a VM on a noisy neighbour hogging shared CPU we might get things wrong as well.

karanraina 2mo ago

yes, for example we dont have connectors for k8s yet, so we are blind to memkills triggered due to sidecars. trying to debug using our tool might even lookup some memleak candidates in your primary container, but there wont be conclusive evidence for it and it would say so. for in app errors all we do is hypothesize and either prove/disprove that using data from running system. and whenever we do report something we give have the evidence for it. its not fool proof but just asking does this hypothesis gets proved with this evidence in a subagent mostly does the trick

IgorVoytyuk 2mo ago
"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.
- karanraina 2mo ago
  i just gave the k8s example to show that we wont be able to find root cause of every issue. our snapshots give evidence, whether or not its helpful is determined by the engineer/ai agent I'm not saying every issue would be diagnosed this way, sometimes, it might well be beyond your control like a VM on a noisy neighbour hogging shared CPU we might get things wrong as well.