AI code debugging starts with better evidence.
Remedy connects what the user experienced with the permitted technical signals that help a coding agent locate the real failure.
Why software bugs are hard to diagnose
“The button does not work” may be accurate, but it does not tell a developer which route, state transition, validation rule or network response caused the failure. Debugging slows down when the person reporting the defect and the engineer investigating it have different views of the same event.
Context-aware AI debugging
Remedy captures the report at the affected screen, adds permitted recent application events and sanitises the payload. That evidence gives AI-assisted debugging a narrower, more relevant starting point than a generic prompt or isolated error message.
From likely cause to testable repair
The diagnosis identifies the suspected component and failure path. Remedy then creates a bounded repair task and associates the proposed fix with checks that matter to that defect. This makes AI debugging part of a traceable engineering workflow rather than a one-off chat.
Useful for difficult-to-reproduce defects
- Intermittent user-interface behaviour
- State and navigation errors
- Form and validation failures
- Unexpected API-response handling
- Mobile device-specific application issues
- Regression defects discovered during acceptance testing