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Use Case

Build self-healing data pipelines with AI agents

Deploy agents that monitor, debug, and repair your data pipelines autonomously. When a pipeline breaks, your agent diagnoses the root cause, applies fixes, and alerts your team.

The Problem

  • Pipeline failures at 3am require on-call engineers to debug
  • Root cause analysis is manual and time-consuming
  • Same failure modes keep recurring without permanent fixes
  • Data freshness SLAs broken by slow incident response

How It Works

  1. 1Connect to your orchestrator: Airflow, Dagster, Prefect, etc.
  2. 2Agent monitors pipeline runs and detects anomalies
  3. 3On failure, diagnoses root cause using logs and metadata
  4. 4Applies known fixes or escalates with detailed diagnosis

Results

  • 90% of pipeline failures resolved without human intervention
  • Mean time to resolution drops from hours to minutes
  • On-call engineers handle only genuinely novel issues
  • Full audit trail of every diagnosis and remediation action

Example Agent Prompt

This Airflow DAG failed at the transform step. Check the logs, identify the root cause, and if it's a known issue apply the fix. Otherwise escalate with a diagnosis.

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