What is LLM Observability?
ObservabilityMonitoring model calls: latency, tokens, errors, per-call cost. Necessary but not sufficient for agents: it sees each request while missing the run. The step below agentic observability.
Why It Matters
LLM observability answers a real question: is each model call healthy? Latency, token counts, error rates, cost per request. For a chatbot, that is most of the story, because one call is one answer.
For an agent it is the wrong unit. A user request fans out into a chain of model calls and tool invocations, and the failures that matter live between the calls: a wrong step contaminating every step after it, a loop that stays green on every individual request while the run burns money. Each call looks fine. The run is the failure.
What It Covers
Latency and errors. Per-call health: how long each request took, which ones failed, what the provider returned.
Token usage. Input and output counts per call, the raw material of the bill.
Per-call cost. The dollar figure attached to each request, so spend has a per-call face.
Eval signals. Quality scores attached to calls where a grader ran.
Where It Breaks
The ceiling is structural: per-call instrumentation produces disconnected fragments, because each service records its own requests and provider SDK calls often go untraced. Tool executions made by an agent framework land outside the application’s span tree entirely. You end up with the agent’s thinking and none of its actions.
And a non-deterministic system cannot be replayed. When the failure was one-off, the per-call records you captured are the only evidence that exists.
How Flytebit Handles It
We treat per-call monitoring as the floor, not the target: agentic AI observability links every call into a single trajectory, so the run is the unit of inspection. The full argument for run-level visibility is in Observability for Agentic AI Governance, and the instrumentation is what our observability engagements install first.