As AI agents evolve from simple chatbots into complex orchestrators using the Model Context Protocol (MCP), they have become the ultimate distributed system "black boxes." When an agent fails to complete a task or enters an infinite reasoning loop, traditional APM metrics like latency and CPU usage offer no clues. We need to see the "why" behind the agent's decisions.

In this technical deep-dive, we demonstrate how to bridge the visibility gap by integrating OpenTelemetry with the MCP ecosystem. We will explore how to implement W3C Trace Context propagation across MCP clients (like Claude or custom agents) and MCP servers (tool providers). Using the latest OpenTelemetry GenAI Semantic Conventions, we’ll show how to capture critical agentic signals: reasoning steps, tool-calling intent, and token consumption.

The session features a live demonstration of a Python-based AI agent calling a TypeScript MCP tool server, linked by a single, unified OTel trace. Attendees will learn how to transform opaque AI "thoughts" into actionable, structured telemetry that can be debugged, audited, and scaled.