How can I track the performance of AI agents and detect hallucinations or other issues?

He suggests two ways to gauge AI‑agent performance. The more thorough method is to create a standardized evaluation set of factual claims, run every agent conversation through it, use a cheaper model to pull out claims, attribute them back to the original conversation via unique IDs, then verify the claims with a stronger model. This gives you an automated hallucination check. A simpler, business‑focused approach is to compare financial results before and after replacing human work with the AI agent: if revenue stays the same or increases, the agent is delivering value; if revenue drops, the AI is costing you more than it saves. Ultimately, AI should be a revenue benefit, not just a cost‑saving metric.