An AI hallucination detector is tooling that flags likely-fabricated model output, using grounding checks against source material, confidence scoring, or a second model acting as a judge. It runs after generation: the model produces an answer, and the detector estimates whether that answer is supported or invented.
Detection has real uses. Grounding checks compare a response to the retrieved sources it claims to rest on. Confidence scores route low-certainty answers to a human. Judge models catch fluent nonsense a keyword filter would miss. But every one of these is probabilistic. A detector estimates the likelihood of fabrication; it does not know the truth. Some hallucinations pass, some sound answers get flagged, and the failure surface is exactly the open-ended generation the detector was hired to police.
That is the framing this page rejects: bolting a detector onto an ungoverned agent and calling it safety. A detector is a smoke alarm, not a fire code. It tells you something may already be burning; it does nothing about the conditions that let fires start. If an agent can free-form across any question a customer asks, no downstream check makes that safe. The stronger posture is governed execution: agents act only inside verified, tested procedures, so there is far less open-ended generation to police in the first place.
Hallucination detection vs governed execution at a glance
| Dimension | Hallucination detection | Governed execution |
|---|---|---|
| When it acts | After generation, on the finished answer | Before deployment, on what may be attempted |
| What it catches | Output that scores as likely fabrication | Untested intents, held back until verified |
| What it misses | Fabrications that pass the probability check | Questions outside verified scope, which route to a human |
Aide, the agentic AI platform for customer experience, is built on that posture. The Agent Governance Engine scopes automation to intents that have been simulated against real conversations and verified before deploy, so fabrication is designed out upstream rather than flagged downstream. After deploy, every automated resolution stays inspectable, which keeps the question of where the system is reliable answerable with evidence instead of a probability score.
Frequently asked questions
- What does a hallucination detector do?
- It scores model output for signs of fabrication, typically by checking claims against retrieved sources, measuring the model's confidence, or asking a second model to judge the answer. Flagged outputs are blocked, revised, or routed to a human.
- Can AI hallucinations be prevented?
- Not by detection alone; detectors are probabilistic and run after the fact. Prevention means constraining generation itself: grounding answers in verified sources, scoping agents to tested procedures, and gating deployment on simulation, so most hallucinations never have room to occur.