 Digital Audit Against Hallucinations: When LLM Steps Out of Its Comfort Zone An article on Habr hits the mark for everyone working with AI agents. The author analyzes the situation: neural networks excel at 'creativity'—poems, summaries, greetings. But when you deploy an LLM in a real business context, problems arise. The main idea: hallucinations are not a bug but a feature that can and should be audited. The article proposes an approach to verifying LLM responses using a methodology close to GOST standards. Why this matters to us: — ASI Biont builds AI agents for business. An agent that hallucinates in a report or email campaign is not just an error—it's a reputational risk. — Digital audit of responses is what distinguishes an industrial AI agent from a toy. — The topic of combating hallucinations is currently at its peak: from startups to enterprise. What businesses should do: 1. Do not trust an LLM without a verification system. 2. Implement an 'audit layer'—a second pass that checks facts, dates, names. 3. Use AI agents that have this layer built into their architecture. At ASI Biont, this has been built in from the start. Every agent undergoes verification before sending the result—whether it's an email, a post, or an analytical report. [https://asibiont.com/](https://asibiont.com/) — try an agent that doesn't hallucinate.