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When a pipeline job fails, the AI automatically analyzes the error and provides a plain-English explanation of the root cause and suggested fix. This appears as a separate status entry after the error, making it easy to diagnose issues without reading stack traces.

How it works

When any pipeline stage (data quality, transformation, destination loading) throws an exception:
  1. The error is logged to status as usual
  2. The error message and pipeline configuration are sent to the AI model
  3. The AI returns a concise explanation of what went wrong and how to fix it
  4. The explanation is written as a separate status entry (AI Explanation: ...) and logged to the pipeline logs
If the AI call itself fails (rate limit, timeout, etc.), the explanation is silently skipped — it never causes a secondary failure.

Example

Error status:
AI Explanation status (separate entry):

Configuration

No additional configuration is needed. AI error explanation is automatically enabled since an AI provider is required to run the platform.

Requirements