Can an AI Agent Fix a Failed Data Pipeline? Build a Safe Recovery Workflow

Description and use

<p>A data pipeline fails at 2 AM.</p> <p>An AWS Glue job runs out of memory. Step Functions marks the execution as failed. CloudWatch has the logs, but someone still needs to investigate what happened, identify the cause, decide what to do next, and rerun the job safely.</p> <p>Now imagine an AI agent helping with that process.</p> <p>It checks the failed execution, examines relevant logs, identifies a likely cause, recommends a recovery action, and asks for approval before making a production change.</p> <p>Sounds useful, right?</p> <p>But there is one important difference between building an

Problem

<p>A data pipeline fails at 2 AM.</p> <p>An AWS Glue job runs out of memory. Step Functions marks the execution as failed. CloudWatch has the logs, but someone still needs to investigate what happened, identify the cause, decide what to do next, and rerun the job safely.</p> <p>Now imagine an AI agent helping with that process.</p> <p>It checks the failed execution, examines relevant logs, identifies a likely cause, recommends a recovery action, and asks for approval before making a production change.</p> <p>Sounds useful, right?</p> <p>But there is one important difference between building an

Outcome

<p>A data pipeline fails at 2 AM.</p> <p>An AWS Glue job runs out of memory. Step Functions marks the execution as failed. CloudWatch has the logs, but someone still needs to investigate what happened, identify the cause, decide what to do next, and rerun the job safely.</p> <p>Now imagine an AI agent helping with that process.</p> <p>It checks the failed execution, examines relevant logs, identifies a likely cause, recommends a recovery action, and asks for approval before making a production change.</p> <p>Sounds useful, right?</p> <p>But there is one important difference between building an

Evidence

Can an AI Agent Fix a Failed Data Pipeline? Build a Safe Recovery Workflow

Source