How do you handle AI agent "hallucinations" using AgentOps?

All answers to this question.

Hallucinations are the biggest hurdle for us right now. With AgentOps, we implement "trajectory analysis." We don't just look at the final answer; we monitor the intermediate steps. If an agent claims it fetched data from a source but the logs show the API call failed, the monitoring system flags a "logic mismatch." In early 2024, we set up automated evaluations where a "critic agent" reviews the primary agent's work. This multi-layered oversight is a core part of the operations stack that keeps our production environment stable and trustworthy.

   Answered 2025-07-07 by Victoria Hayes

Victoria, how much latency does this "critic agent" review add to the overall process? Is it viable for real-time customer-facing agents?

   Answered 2025-07-09 by Samuel Porter

• Samuel, it does add about 1-2 seconds of latency. For real-time chat, we use "lightweight" guardrails, and for background tasks, we use the full AgentOps review suite. It’s a trade-off.

     Commented 2025-07-10 by Douglas Grant

We use AgentOps to log every prompt and response, which allows us to run "replay debugging" to find exactly where the hallucination started in the chain

   Answered 2025-07-11 by Diane Murphy

• Replay debugging is a game changer, Diane. Being able to step through an agent's logic like a standard code debugger makes fixing these LLM quirks much faster.

     Commented 2025-07-12 by Patrick Flynn