agent-observability

You monitor AI agent token usage and latency by instrumenting the agent with trace IDs and capturing LLM calls as child spans. This structured logging tracks request latency, token metrics, and tool call success rates for reliable debugging.

AI agent observability is the process of tracking core signals like request latency, token usage, cost, and hallucination frequency. You need it when troubleshooting agent reliability issues or managing operational expenses for production AI assistants.

You track cost per task and customer by implementing structured tracing that captures token metrics and tool call data. This visibility allows product teams to set alerts for unexpected token spikes and control operational costs.

Yes, you can identify hallucinations and retry frequency by instrumenting your agent with trace IDs and child spans. This reliability monitoring captures tool call failures and retry patterns to improve overall agent stability.

The best way to trace tool call success and failure rates is by implementing a tracing pattern with trace IDs and capturing tool calls as child spans. This structured logging provides clear visibility into tool call performance and reliability.