Advanced · Observability & Monitoring
Section 1 of 4

Why Observability Matters for LLM Apps

Traditional software is deterministic: the same input produces the same output, so a passing test suite tells you a lot. LLM apps aren't — the same prompt can take a different path, call a different tool, take longer, or cost more from one run to the next.

That unpredictability is invisible in a demo that just works. It becomes a real problem the moment other people depend on the app: a slow response, a runaway agent loop, or a cost spike needs to be visible before a user has to report it, not discovered by staring at raw logs after the fact.

Try it: the same agent, now instrumented

This runs the exact agent loop from the AI Agents module — the only difference is that every LLM call and tool call is now timed, token-counted, and priced.