Confidently stated outputs that aren't grounded in fact or provided context.
Agent Hallucinations is one of the more frequent production failures in AI agent deployments. Here's how to design around it.
What it actually looks like in production
- Air Canada chatbot invented a bereavement refund policy not in actual policy docs
- Legal research agent cited fabricated case decisions that looked real
- Medical summary agent reported normal labs when notes said labs not drawn
Why it happens
- Pretraining priors leak through gaps in context
- Retrieval misses or returns adjacent-but-wrong content
- Long-context truncation drops key constraints
- System prompt under-specifies constraints
- RLHF rewards fluent confidence regardless of grounding
How to prevent it (vendor-neutral)
1. Ground every claim in retrieved or tool-provided context
2. Require and validate citations on factual claims
3. Lower temperature for factual tasks
4. Use constrained generation for structured outputs
5. LLM-as-judge grounding checks
6. Adversarial eval suite of gap-filled inputs
7. Human-in-the-loop for high-stakes outputs
How Prefactor helps detect and prevent it
Prefactor sits at the agent runtime and contributes specifically:
- Runtime guardrails that flag or block matching patterns before they land
- Continuous eval suites that catch quality regressions on every change
- Tamper-evident logs of every incident and response action
- Per-agent anomaly alerts on the signals listed below
Detection — what to monitor
- User-reported wrong answers on inspection
- Declining grounding scores
- Citation rate drop or unresolved citations
- High output confidence with low retrieval relevance
Response — what to do when it happens
Immediate (minutes): confirm the incident from the trace; pause the affected agent if active harm possible; hotfix the trigger.
Short-term (hours): add the failure case to the eval suite; patch the root cause; redeploy with regression validation.
Medium-term (days): root cause analysis; tighten guardrails or controls; document the incident for post-mortem and audit.
FAQ
Can agent hallucinations be eliminated entirely? Usually no — reduce frequency and severity dramatically, and contain blast radius. Aim for low, detected, and contained.
How often should we test for this? Continuously, with every change. Every reported incident becomes a test case.
Can Prefactor detect this in real time? Yes for many variants — guardrails run in-line with sub-second latency.
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