In 2025, Anthropic partnered with AI safety company Andon Labs on an interesting experiment: allowing its AI model Claude, nicknamed “Claudius,” to take full control of an unmanned office store. From setting prices and managing inventory to responding to employees’ shopping requests via Slack, everything was left to the AI to decide. The results, however, were both amusing and revealing——Claudius not only gave away so many discounts that it essentially negotiated away its entire profit margin, but at one point it even “decided” that there was an employee named Sarah, and proceeded to interact with and discuss inventory and payment arrangements with this person who did not actually exist. This is a well-known yet challenging phenomenon in the field of AI: Hallucination.

LLM hallucination refers to a situation in which a large language model generates a response that is fluent in language and seemingly logical, but is actually incorrect or unsupported by evidence. This is different from “lying,” because an LLM has no intention to deceive. At its core, an LLM operates by predicting the “most likely next word” based on probabilities, rather than verifying facts like a database would. When it encounters a question for which its training data provides no relevant information, or when the current context lacks sufficient evidence, it may “fill in the gaps” with an answer that sounds reasonable but cannot withstand scrutiny——just as Claudius imagined the existence of an employee named Sarah out of thin air.

Common forms of hallucination in enterprise applications include “factual hallucinations,” in which rules or figures are stated incorrectly; “fabricated sources,” in which nonexistent clauses or documents are cited; “logical inconsistencies,” in which statements contradict one another within the same conversation; and “overconfidence,” in which the AI responds with certainty even when it is actually unsure of the answer. For general-purpose chat tools, these issues may simply become amusing anecdotes; but when they occur in enterprise AI customer service scenarios, the consequences can be entirely different.

Imagine if an AI customer service agent were “overly generous” like Claudius and, without authorization, promised customers discounts or refund conditions that the company does not actually offer. What would happen when customers later demanded that the company honor those promises? If an AI provided incorrect information about insurance policy terms, return periods, or financial product regulations, it could lead not only to customer complaints but also to legal and compliance risks. Once customers catch an AI “saying something wrong,” their trust in the entire service brand can plummet, while the manpower and reputational costs of correcting the problem afterward may far exceed the savings that the company originally expected from implementing AI customer service.

To reduce the risk of hallucinations, enterprises do not have to choose between two undesirable options——the key is to “ensure that AI responses are supported by reliable evidence.” Through a RAG (Retrieval-Augmented Generation) architecture, AI responses can be grounded in the company’s actual knowledge base and the latest rules and policies, rather than allowing the model to freely generate answers based on its training memory. This is currently one of the industry’s most widely adopted approaches. In addition, for high-risk topics such as legal, medical, and financial matters, enterprises should establish a safeguard mechanism that allows the AI to “transfer the conversation to a human agent when it is uncertain.” Combined with human-AI collaboration and continuous error feedback, this approach can help balance both the efficiency and reliability of AI customer service.

The Claudius experiment does not tell us that “AI cannot be used.” Rather, it reminds enterprises that the more autonomous and fluent AI becomes, the more it needs to be constrained by a well-designed framework of governance and oversight. Implementing AI customer service has never been an all-or-nothing decision; instead, it is about deploying AI gradually and incrementally while keeping risks under control.