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AI-Assisted Debugging

AI debugging works when you feed runtime evidence — exact errors, logs, repro steps — and constrain the agent to hypothesize before editing.

Last reviewed: June 2026

Debug mode naming varies by tool. See Cursor Debug and your IDE's docs.

Debug loop

flowchart LR
    repro[Reproduce failure] --> capture[Capture exact error output]
    capture --> hypothesis[Agent proposes hypothesis]
    hypothesis --> fix[Minimal fix in scoped files]
    fix --> verify[Re-run failing command]
    verify -->|fail| capture
    verify -->|pass| review[Human review diff]

Never skip reproduce — agents fix symptoms when they cannot run the failing command.

Prompt template

Debug mode — do not refactor unrelated code.

Failure:
<paste exact stderr / stack trace>

Repro:
1. npm test -- src/foo.test.ts
2. Fails with: Expected X, received Y

Files involved:
@src/foo.ts
@src/foo.test.ts

Hypothesize root cause first. Then propose a minimal fix.
List every file you would change before editing.

What to attach

EvidenceValue
Full stack traceHigh — pinpoints line
Failing test outputHigh — expected vs actual
Recent git diffMedium — regression source
Network HAR / API responseMedium — integration bugs
ScreenshotLow for backend; useful for UI

See Context Engineering.

Anti-patterns

Anti-patternWhy it fails
"Fix the bug" with no error textModel guesses
Large refactor while debuggingHides root cause
Trust fix without re-running testHallucinated fix
Multiple bugs in one promptAgent fixes one, breaks another

Tool-specific tips

ToolTip
Cursor DebugDesigned for runtime evidence workflow
Claude CodePaste CI log; run test command in same session
Copilot ChatGood for explanation; verify with tests
Aider/add only failing files; run tests externally

When to stop using AI

  • Heisenbugs / timing issues — use logging and profilers first
  • Production incidents — human-led; AI summarizes logs only
  • Security vulnerabilities — human-led fix; AI for checklist review