How to Detect AI Hallucinations in Agent Output

AI agents that hallucinate facts, invent citations, or fabricate data pose real risks. In agentic systems, hallucinated output does not just mislead a reader. It can trigger tool calls, database writes, or financial transactions based on information that does not exist. Detecting hallucinations at runtime is essential for production safety.

Types of Agent Hallucinations

Factual hallucinations occur when the model states something objectively false. An agent might claim a user has a specific account balance or that a regulation requires something it does not.

Tool result hallucinations happen when an agent fabricates the output of a tool call it never made, or misrepresents the result of one it did make.

Citation hallucinations involve references to documents, URLs, or data sources that do not exist.

Detection Strategies

Cross-reference verification compares agent claims against ground truth. If an agent says it queried a database, verify the query actually ran and returned the claimed result. Authensor's receipt chain captures every tool invocation, making this verification straightforward.

Consistency checking runs the same prompt multiple times at low temperature. If outputs diverge significantly, the content in question is likely hallucinated rather than grounded.

Confidence calibration examines token-level log probabilities when available. Sequences with consistently low confidence scores across tokens deserve extra scrutiny.

Structured output validation forces agents to return JSON that conforms to a schema. Fields that fail validation or contain unexpected values flag potential hallucinations.

Runtime Integration

Build hallucination checks into your agent pipeline as a post-processing step. Authensor's Sentinel monitoring can track hallucination rates over time, alerting you when an agent's reliability degrades. Combine automated detection with periodic human review of flagged outputs to calibrate your detection thresholds.

Prevention is better than detection. Use retrieval-augmented generation, constrain tool access, and keep context windows focused on relevant information.