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What’s the biggest problem you’ve faced with AI hallucinations in real work?

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Not long ago we had a good discussion here about production AI agents and how hard it is to move from demo to reality.

I really enjoyed reading everyone’s war stories. Now I want to zoom in on one specific pain that keeps biting teams.

Founders, engineers, and operators running AI agents — what’s your current approach to handling hallucinations and confident-but-wrong answers?

I’ll go first.

Last month one of our agents confidently invented a non-existent policy and almost caused a serious internal mistake. It wasn’t a small hallucination — it was presented with such certainty that a team member was ready to act on it. We caught it in time, but it shook our trust.

For a while we were obsessed with the usual metrics: resolution rate, handoff percentage, speed. Turns out optimizing only for those can be dangerous.

So we started tracking something new: Confident Wrong Answers (CWA) — every time the agent gives a definitive answer that later turns out to be fabricated or incorrect.

The trade-off was painful but necessary:

  • Resolution rate dropped ~14%

  • But dangerous errors dropped dramatically

  • Team trust in the system actually increased

The realization for me was clear: In real work environments, being confidently wrong is much more damaging than honestly saying “I don’t know.”

Hallucinations don’t just create annoying mistakes — they quietly destroy the reliability of information inside the company. Once people get burned a couple of times, they stop using the tool.

I’d love to hear from you:

  • What guardrails or metrics have actually helped you reduce hallucinations in production?

  • Have you also had to sacrifice some “performance” to gain trustworthiness?

  • Or are you still mostly relying on better RAG / prompt engineering?

Looking forward to your experiences.

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Really good thread. Here's a concrete one from our side. Early on, a user asked NEXIA for a structural sizing calculation but left out one critical dimension. Instead of stopping, NEXIA quietly filled in a plausible value on its own, ran the whole calculation on it, and even backed it up with a technical-standard reference number that — we later found out — didn't exist. The scary part wasn't the error itself, it was how clean and confident the output looked. A user could have sent that straight to a client. Three protocols we put in place after that, and we haven't seen it since: 1. Never invent a critical input. If a dimension, a value or a constraint is missing, NEXIA is required to stop and ask — not to guess. A missing input is a question, never a blank to fill. 2. Reason before answering. It has to lay out its reasoning before committing to a result, so the "wait, I don't actually have this number" moment happens before the answer, not after. 3. No source, no claim. Any standard, number or rule that can't be grounded in a real source is flagged as an assumption, not stated as fact. Net effect is exactly what you described: slightly less "instant magic," a lot more trust. And trust is the only thing that keeps people using the tool. That principle is basically why we built NEXIA the way we did — an AI that thinks before it answers instead of one that just sounds confident.

 first of all sorry for late reply since I missed your message

That "reason before answering" protocol is the one I keep coming back to. It's basically forcing the model to show its work before committing, so the gap in its own logic surfaces before it becomes someone else's problem. We've been doing something similar and it genuinely changes the failure mode from "confident wrong answer" to "the agent flagged uncertainty and stopped" which is a completely different kind of failure to debug.

The "no source, no claim" rule is deceptively hard to enforce at scale though. In our experience the model still finds ways to hallucinate the source itself, not just the claim. So we had to add a retrieval verification step that checks whether the cited source actually exists in the knowledge base before the answer goes out. Adds latency but it's worth it.