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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. The result — labelled AI Suggested Fix in the job status — appears as a separate status entry after the error, making it easy to diagnose issues without reading stack traces.

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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 Suggested Fix: ..., with Diagnosis: and Suggested fix: lines) and logged to the pipeline logs
If the AI call itself fails (rate limit, timeout, a reply that is not the expected JSON), the explanation is skipped: the server logs a warning (Failed to get AI fix suggestion (best-effort, continuing without it), with the exception), no AI Suggested Fix entry is written, and the run’s error status — already recorded before the suggestion is requested — stands unchanged. The job’s outcome is never affected by the suggestion step. When AI is disabled, the step is skipped silently with no warning.

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Example

Error status:
AI Suggested Fix status (separate entry):

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Configuration

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

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Requirements