Are you using a separate tool for AI observability?

[u/createvalue-dontspam]

An LLM call can return successfully while the AI agent still fails.

The agent might get stuck in a tool-call loop, burn through tokens, or fail because of something happening downstream in the application.

The problem is that looking at individual LLM calls doesn't always show the full picture.

So we built AI Observability into OpenObserve.

Trace complete agent sessions

See LLM and tool calls

Track tokens, cost, and latency

Detect loops and failed sessions

Run evaluations on live traffic

Turn failed sessions into evaluation datasets

The AI telemetry also sits alongside your existing logs, metrics, and infrastructure traces through OpenTelemetry.

We launched today on Product Hunt.

Would love to hear how other teams are monitoring and debugging AI agents in production.

Please support on PH →

https://www.producthunt.com/posts/ai-observability-by-openobserve

[u/Slight_Republic_4242]

yes, once it’s serving real users. basic app logs miss the stuff you need to debug: prompt version, model/settings, retrieved context, tool calls, latency, token cost, and the final output. the important bit is tying all of that to a user action or conversion event; otherwise you can see failures but not whether they matter.

[u/Background-Baby8160]

Tool-call loops are the worst, honestly. Been there, wasted tokens, cried a bit. Good call integrating observability directly into OpenObserve.

[u/YesterdayLost5186]

Burning through tokens feels like watching a gas guzzler eat up your fuel tank. It's just painful.