ai-observability-and-metrics

To add Prometheus metrics for AI agent tool calls, you instrument counters and histograms using prom-client conventions to track tool call durations and success rates, enabling performance analysis and issue identification.

Braintrust tracing works by implementing detailed tracing spans for requests, tool calls, verification tiers, and LLM interactions, allowing you to monitor the internal workings and latency of each step in the AI agent execution process.

Yes, you can define new metrics with specific labels for the AI module by adhering to prom-client conventions, allowing you to track custom agent actions, durations, and states while maintaining guardrails against insecure metric exposure.

The best way to debug AI agent behavior is by implementing observability through both Prometheus metrics and Braintrust tracing, which provides dashboards for success rates and detailed traces for tool execution latency.

Yes, when instrumenting AI agent observability, you must maintain strict guardrails against PII logging and insecure metric exposure to ensure sensitive user data is not leaked through Prometheus metrics or Braintrust traces.