Why AI agents sometimes make things up and the practical controls that keep business outputs accurate.
The short answer
Hallucinations happen when a model fills gaps with plausible but false information. In business workflows, prevent them by grounding agents in your data, requiring sources and keeping human approval.
Key takeaways
Models predict likely text. Without the right data, they guess.
Ground answers in connected systems, ask the agent to cite sources, restrict it to known fields and flag uncertainty.
A reviewer catches what controls miss. That's why every Switch output is approved by a person.
Not fully, but grounding and review reduce them to manageable levels.
Yes, but workflow design matters more.
How this guide was made
Written and reviewed by our editors against vendor documentation, public regulation texts and practitioner interviews. We update guides when products or rules change. Tools mentioned may have a commercial relationship with this site; this never decides what we recommend.
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