Meet Jax. He's a Lead AI Engineer who just deployed an autonomous customer support agent. Everything seemed fine—the LLM outputs looked great in testing. But in production, the agent is confidently issuing wrong refunds and getting stuck in infinite loops.
Jax thought standard LLM monitoring (tracking prompt inputs and outputs) would be enough. He was wrong..
The Old Way: Just tracks a single prompt in and a single text response out. Good for chatbots, terrible for autonomous agents.
The New Reality: Tracks the entire execution trajectory—multiple reasoning steps, API tool calls, state adjustments, and internal loops. This is what Jax actually needs.
Visual comparison of a single prompt-response interaction versus a multi-step agent trace with tools, branches, and internal state changes.
