Agentic AI for Data Pipelines: Monitoring, Repair, and Exception Handling
Data pipelines fail in predictable ways, but diagnosing them can still take hours.
Agents help by reducing the search space.
Where agents help most
- Detecting anomalies and failures
- Gathering logs and context
- Suggesting root causes
- Drafting remediation steps
- Filing tickets with complete details
Keep fixes gated
Agents can propose fixes, but production changes should be approved.
A safe pattern:
- Detect issue
- Gather evidence
- Propose likely causes
- Suggest fix options
- Human selects and approves
- Agent executes the approved change
What to log
- Which pipeline
- Failure type
- Last successful run
- Dependencies involved
- Proposed fix and rationale
This creates a knowledge base over time.
Closing thought
The goal is not an autonomous pipeline repair robot.
The goal is faster diagnosis, cleaner handoffs, and fewer repeats.