How Uber’s Debug Assist Uses AI to Diagnose and Fix Production Issues

Uber’s Debug Assist is an AI-assisted system designed to help engineers investigate production issues and move from reports toward actionable fixes. I explain the challenges it addresses, the system’s workflow and architecture, and examples of how it is used at Uber.

■ Problem and workflow
- How Wisdom and Healthline capture issue reports, logs, and diagnostic context; how issues are classified and routed
- Examples involving battery drain and a crash, followed by an overview of root-cause analysis and proposed fixes

■ Architecture and deployment
- The LangGraph-based workflow, deterministic and LLM-powered steps, specialist agents, and safeguards
- Shared infrastructure, pluggable skills, developer review tools, validation, and integration with internal systems

■ Results and discussion
- Reported operational metrics, followed by audience questions about testing strategies and coordinating specialist agents

The presentation focuses on Uber’s approach and architecture rather than offering a step-by-step implementation tutorial. It is intended for software engineers and engineering leaders; viewers can use the workflow and design considerations to assess how AI-assisted debugging might fit their own incident-response processes.