BugSnag + SmartBear MCP: Agentic Debugging Demo

Production errors don’t wait for engineers to context-switch, and increasingly, they don’t have to.  In this live session, we’ll show how SmartBear MCP connects BugSnag’s production error and performance data directly into AI-powered tools like Claude, Cursor, GitHub Copilot, and Codex. Instead of jumping between dashboards, logs, and traces, developers can pull in full runtime context — stack traces, event payloads, affected users, and span data — directly inside their IDE.

This isn’t a vision talk. It’s a working example of how observability becomes an input to action, not just analysis.

Rosemary Charnley, Solutions Engineer at SmartBear, will run the workflow end-to-end: starting with a natural language question about recent production issues, drilling into a specific error, identifying root cause, and generating a proposed fix that can be turned into a pull request.