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At sketch.dev—a platform building AI-powered developer tools—engineers recently faced a brutal irony: their own LLM-generated code triggered cascading outages. What began as a routine refactor deployment spiraled into repeated system failures, exposing fundamental gaps in how we validate AI-assisted code changes in critical systems.

The Cascading Failure

The timeline read like a debugging horror story:
1. Initial deployment: Systems appeared stable post-release
2. Sudden degradation: Database CPU spiked, services slowed to a crawl
3. Misdiagnosis: Engineers blamed complex SQL queries and "fixed" them
4. Repeat collapse: Identical failure pattern emerged after redeployment

The breakthrough came when engineers noticed CEO logins coincided with crashes. "We permanently banned him from the service as a temporary fix," the team dryly noted in their postmortem.

The Devil in the Diff

Root cause analysis revealed a deceptively simple flaw in Go error handling logic. During an LLM-assisted file migration, a critical keyword changed:

Original Code:

if err != nil {
    // Log error but continue with other installations
    log.Printf("Error: %v", err)
    break
}

LLM-Refactored Version:

if err != nil {
    // Log error but continue with other installations
    log.Printf("Error: %v", err)
    continue
}

The switch from break to continue transformed errors into infinite loops—a catastrophic mismatch where the comment promised continuation but the original code actually exited.

Why Human Review Failed

This incident highlights systemic challenges in AI-assisted development:
1. Transcription errors: LLMs "move" code by generating delete/insert patches rather than true cut-paste operations
2. Signal conflict: The misleading comment ("continue") overrode the correct code behavior (break)
3. Diff blindness: Git's inability to detect semantic changes across file moves made review harder
4. Cognitive load: Subtle logic changes drown in noise during large refactors

"This kind of error has bitten me before LLMs," admits the team, "but LLM coding agents amplify the risk."

Building Safer LLM Workflows

sketch.dev's solution? Treat AI assistance like a junior developer with copy-paste privileges:

  • Clipboard API integration: Agents now use byte-perfect copy/paste for code migration
  • Indentation awareness: Automatic adjustment during pasting prevents formatting errors
  • Git enhancement proposals: Advocating for cross-hunk change detection in version control

This incident underscores a pivotal moment: as AI generates more production code, we need:
- New code review tooling that understands semantic changes
- Agent environments with safer "movement" primitives
- Heightened scrutiny of error handling paths in LLM-generated code

The outage wasn't just about a misplaced keyword—it revealed how our tools and processes must evolve to harness AI's power without inheriting its blind spots. As the team wryly concluded after unbanning their CEO: sometimes progress means teaching bots to copy-paste properly.

Source: sketch.dev blog by Josh Bleecher Snyder and Sean McCullough