BugSnag + SmartBear MCP: Agentic Debugging Demo

From Alert to Root Cause in Minutes: Agentic Debugging with BugSnag and SmartBear MCP 

See how SmartBear MCP pulls BugSnag’s production error and performance data — stack traces, event details, affected users, and span details — directly into your team’s Agents or AI-powered IDE, so developers can triage, investigate, and fix issues without ever leaving their flow.

Overview

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. 

A SmartBear Solutions Engineer 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.

What you’ll learn

• How MCP connects BugSnag into the AI tools your developers already live in — Claude, Cursor, GitHub Copilot, and other MCP-compatible clients 
• A live end-to-end triage: “What errors happened this week?” → full event and stack trace → root cause explanation → suggested fix 
• Where agentic debugging actually delivers value today — on-call, incident response, and backlog grooming 
• What it takes to get started with MCP in your environment, and the practical constraints to be aware of 
• The patterns engineering leaders are using to adopt AI-assisted debugging without slowing teams down or adding process overhead 

Who should attend?

• Engineering leaders accountable for reducing time-to-resolution, not just improving observability coverage 
• Platform, DevOps, and SRE teams responsible for incident response and developer workflow efficiency 
• Senior engineers who are tired of stitching together logs, traces, and error data across tools 
• Teams already experimenting with MCP, Copilot, Cursor, or Claude and looking for high-value, production-grade use cases 
• Organizations evaluating whether AI can move debugging from analysis to actual remediation