Production AI agents make thousands of tool-calling decisions daily, yet observability stops at the model boundary. OpenTelemetry's GenAI semantic conventions capture token counts and latencies—what the LLM processed—but not why an agent selected a specific tool. Research (McKenzie et al., 2023) demonstrates inverse scaling: more capable models exhibit unpredictable tool selection patterns. This gap leaves engineers guessing during critical production failures.

We present gen-ai-otel, an open-source OpenTelemetry extension introducing decision-level telemetry for MCP agents. A new attribute namespace (gen_ai.agent.*) captures tool selection confidence, session context, permission scope validation, and baseline deviations. The zero-sidecar architecture routes telemetry through standard Collector pipelines to existing backends—Jaeger, Prometheus, or graph databases—with low overhead and cardinality-aware attributes.

A live demo reconstructs an agent's decision chain, revealing anomalies invisible to token metrics—reducing decision-debugging time. Attendees leave with: 1) Collector configs, 2) Grafana dashboards for confidence tracking, 3) demo code and repo—all Apache 2.0 licensed.