Observability Summit North America 2026

Traditional observability assumes humans operate systems. AI agents break that model—they make autonomous decisions, execute operations without approval, and drift in capability over time. Yet most organizations have zero observability into their AI agent infrastructure.

When developers spin up MCP servers through Claude Desktop or Cursor, security and ops teams are blind. No metrics. No traces. No logs. Just autonomous agents accessing databases, calling APIs, and modifying production systems—completely outside your observability stack.

This talk explores how to instrument AI agents and MCP servers using familiar CNCF tools. We'll cover:

• Why traditional APM fails for autonomous agents (no request/response, emergent behavior, capability drift)
• Detecting anomalies in agent behavior (statistical baselines vs. ML-driven detection)
• Correlating agent actions to business outcomes

You'll see working demos of agent observability plus open-source code for instrumenting LangChain, CrewAI, and custom agents.

Walk away with patterns to extend your existing observability stack to AI agents before they become your biggest blind spot.