Can AgentOps improve the accuracy of a RAG pipeline?

All answers to this question.

Implementing AgentOps was the single biggest boost to our RAG accuracy this year. Before we had it, we just knew the "answer was wrong," but we didn't know why. Now, we can see the full "trace": the query the agent generated, the chunks the vector DB returned, and how the model interpreted them. We discovered that our "accuracy" issues were actually due to poor chunking strategies that were cutting off important context. Having that visibility allows you to iterate on your retrieval logic with data-driven insights rather than just guessing why the bot is hallucinating.

   Answered 2025-05-05 by Pamela Scott

Pamela, are you using any specific automated evaluation frameworks within your AgentOps stack to score these traces for accuracy?

   Answered 2025-05-07 by Gerald Vance

• Gerald, we use a "faithfulness" score. It’s an automated check to see if the final answer can be fully supported by the retrieved chunks. If the score is low, we flag it for manual review.

     Commented 2025-05-08 by Scott Miller

I think the best part of AgentOps is the "Session Replay." Being able to step through a failing query exactly as the user experienced it is invaluable for debugging.

   Answered 2025-05-09 by Alice Cooper

• Completely agree, Alice. Replays turn a "black box" failure into a clear technical bug that you can actually fix in the next sprint.

     Commented 2025-05-10 by Justin Case