Engineer Finds AI Bug Fixes Hinge on Ticket Classification, Not Automation Alone

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Design engineer James Coombs built an AI-assisted pipeline that handles full-stack bug fixes across backend, API, and frontend layers in production. The system uses four stages — Select, Classify, Dispatch, and Monitor — with tickets sorted into tiers based on complexity and integration scope. A key lesson emerged when a ticket labelled a simple 'error handling improvement' actually required changes across three layers, taking 5 hours and 17 iterations instead of the expected 70 minutes. To prevent such misrouting, the pipeline applies a rule that any ticket touching three or more integration layers is automatically escalated to a higher supervision tier before work begins. Beyond fixing individual bugs, the pipeline's most valuable output proved to be the discovery of broader systemic issues — such as widespread exception-swallowing across API endpoints — that were not part of the original tickets.

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