optimize-trace-observability

Sparse OpenTelemetry traces are fixed by instrumenting missing span types and business-critical events. This skill analyzes existing Coval trace data and agent source code to automatically identify and inject missing STT, LLM, TTS, and tool spans into your telemetry.

AI agent traces often lack business-critical events because standard instrumentation does not cover custom workflows. This skill greps your agent codebase to surface missing events like payments or handoffs and adds numeric attributes to track metrics like tool latency directly.

To enrich flat observability traces, you instrument missing span types and map business-critical events within your agent codebase. This process transforms low-quality telemetry into structured traces with meaningful hierarchy and numeric attributes for performance monitoring.

Yes, improving OpenTelemetry trace quality requires access to the agent source code and existing Coval trace data. This access is necessary to identify where to implement missing span attributes and high-value business events within your production-grade workflows.

The best way to add STT, LLM, and TTS spans to telemetry is by analyzing existing trace data to find gaps and applying recommended span enrichment to the agent codebase. This enables granular performance monitoring and failure mode analysis for production-grade workflows.

Yes, you can track workflow completion rates and tool latency in the Coval dashboard by injecting meaningful hierarchy and numeric attributes into your traces. This requires enriching the agent codebase with missing span types and business-critical events.