n8n lays out layered checks to catch AI agent hallucinations before they trigger the next step
A guide on the n8n blog explains that an AI agent hallucination usually looks like a normal answer, and once it triggers another workflow step it becomes a production problem. It lists four types: factual hallucinations, contextual ones (answers not supported by the supplied context), confabulations, and self-contradictions. It presents detection methods meant to be layered: faithfulness checks that split an answer into claims and compare them with retrieved documents (for example with DeepEval), self-consistency checks across several answers to the same question, semantic entropy to estimate uncertainty, verification against an authoritative source such as a database or API, and confidence thresholds that send risky cases to human approval. In n8n these checks can sit on the canvas, a suspicious output, for example one flagged by a second LLM pass, can be routed to human approval, and the Error Trigger node and execution history help investigate failures.
Otomatzia editorial · Published
Source published: · n8n
