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Earlier quoted context omitted.
Easy fix, just have the LLM generate: { "reasoning": "`cfg` can be nil on line 42; dereferenced without check on line 47", "finding": "Possible nil‑pointer dereference", "confidence": 0.81, "confidence_in_confidence_rating": 0.54, "confidence_in_confidence_rating_in_confidence_rating": 0.12, "confidence_in_confidence_rating_in_confidence_rating_in_confidence_rating": 0.98, // Etc... }
confidence all the way down
"After extensive trial-and-error..." IMO, this is the difference between building deterministic software and non-deterministic software (like an AI agent). It often boils down to randomly making tweaks and evaluating the outcome of those tweaks.
Earlier quoted context omitted.
Do you mean that there is no correlation between confidence and false positives or other errors?
elzbardico is pointing out how the author is having the confidence value generated in the output of the response rather than it being the confidence of the output.
I learned from a recent post ( https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-... ) that finding security issues can take 100+ calls to an LLM to get good signal. So I wonder about agent implementers who are trying to get good signal out of single calls, even if they are specialized ones.
The multi agent thing with different roles is so obviously not a great concept, that I am very hesitant to build towards it, even thought it seems to win out right now. We want a AI that internally does what it needs to do to solve a problem, given a good enough problem description, tools and context. I really do not want to have to worry about breaking up tasks into chunks that are smaller than what I could handle m…
Funny thing is the structured output in the last example. ``` { "reasoning": "`cfg` can be nil on line 42; dereferenced without check on line 47", "finding": "Possible nil‑pointer dereference", "confidence": 0.81 } ``` You know the confidence value is completely bogus, don't you?
The problem is that, regardless of how you try to use "micro-agents " as a marketing term, LLMs are instructed to return a result. They will always try to come up with something. The example provided was a poor one. The comment from LLM was solid. Why would you comment out a step in the pipeline instead of just deleting it? I would comment the same in a PR.
I've found that giving agents an "opt out" works pretty well. For structured outputs, making fields optional isn't usually enough. Providing an additional field for it to dump some output, along with a description for how/when it should be used, covers several issues around this problem. I'm not claiming this would solve the specific issues discussed in the post. Just a potentially helpful tip for others out there.
Funny thing is the structured output in the last example. ``` { "reasoning": "`cfg` can be nil on line 42; dereferenced without check on line 47", "finding": "Possible nil‑pointer dereference", "confidence": 0.81 } ``` You know the confidence value is completely bogus, don't you?
This is what I feel like with this blogpost. I've barely scratched the surface of the innards of LLMs but even I know it should be completely obvious to anyone that has a product built around it that these confidence levels are completely made up.
I've never heard or used cubic before today but that part of the blog post, along with the obvious LLM generated quality of it, gives a terrible first impression.