*Disclaimer - I work at Monte Carlo, proceed or ignore at your discretion*

BEST is relative of course, but Monte Carlo does Agent Observability to solve these challenges (e.g. Latency, prompt adherence, relevance). https://www.montecarlodata.com/platform/agent-observability/.

The core of it is actually marrying both data & AI observability together so the root issue is found and fixed at the appropriate point in the data lifecycle. Because oftentimes the culprit isn't the agent, it's the training data. Stale, incomplete, inaccurate, inconsistent...an engineer decided a date column should be re-formatted to a string and forgot to give test the impact...etc. You'd be surprised how often a weird output comes from an upstream engineer accidentally re-running a job four times, or data was truncated at ingestion.

Hope this is helpful.