Why do AI hallucinations persist in production systems?

in production, Hallucinations do not appear as errors: They appear as responses that people initially rely on.

However, this initial trust can be costly.

What we are seeing in actual deployments is that there is no single bug to fix for Hallucinations. They are a system-level behavior that emerges when a few things go wrong simultaneously:

• They are not generated in the model alone. Tool selection, retrieval quality, notation, and orchestration logic can all magnify small uncertainties into convincing lies.
• They go beyond standard monitoring. Accuracy metrics ignore most hallucinations. Signs such as uncertainty, grounding gaps, equipment failure, and confidence mismatches often emerge only after users notice them.
• They engage with feedback and scale. When corrections are not captured (or are misread as a priority), the hallucinations reinforce themselves. Subsequent increased use uncovers cases that never came up in testing.

If your safeguards reside in signals rather than system design, hallucinations are no big deal; They are inevitable.

Our recent article by Maria Piterberg explains why AI hallucinations occur in real systems, and what mature teams do differently to control them.

Worth reading before the next scale-up? If you want to protect yourself from costly errors, of course.