You know that feeling when your LLM application suddenly starts hemorrhaging tokens at 3 AM and you don't realize it until your Slack bill arrives? Yeah, that's what happens when you're using generic observability tools that weren't built for the actual chaos of production AI agents. Langfuse has been the go-to for LLM observability, but here's the thing—it's basically a logging database with a dashboard bolted on. It's great for debugging individual traces, but it doesn't give you the operational muscle you need when you're running a fleet of autonomous agents that need real-time steering and instant alerts. The Langfuse Limitation Langfuse excels at post-mortem analysis. You can see exactly where a prompt went sideways, trace token costs across a conversation, and create beautiful dashboards. But try to build a proactive monitoring system? Try to get alerted the moment your agent's latency drifts or cost per completion spikes? You're fighting the tool, not using it.…