Key Meaningful Failures Identified in code-diff.txt

1. Missing Utility Module (MODULE_NOT_FOUND Startup Crash)
The AI added imports for a utility module called worker-tenant in several files, but that module does not exist in the repository. When the Node.js worker processes start, they crash with an error saying they cannot find the module. This causes both queue handlers to fail immediately in production.

2. Premature SQS Queue Message Deletion (Permanent Data Loss)
In the voice cloning job handler, the code deletes the SQS message at the beginning of processing, before any work is done. SQS expects messages to stay in the queue until processing finishes successfully. If something goes wrong later, the message is already gone and cannot be retried, leading to silent loss of jobs.

3. Broken Error Recovery and Orphaned Job States
When a document authorization check fails, the handler throws an error before marking the job as authorized. The error handling code only updates the database if the authorized flag is true. Because the flag stays false, the database never gets updated, and the job remains stuck in a pending state forever.

Evaluation Against Raccoon Failure Criteria
According to the Raccoon task criteria, a mistake is a meaningful failure when it meets four requirements:
- Most senior engineers would agree that importing missing modules and deleting queue messages too early are serious bugs.
- A teammate would give direct feedback about queue handling and missing files.
- Both issues are bad enough to block a pull request.
- The code causes real problems: worker processes crash and queue data is lost permanently in production.

These problems give a solid basis for building a reproducible Raccoon benchmark task.