Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation

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Every data engineer knows the 2 AM pipeline failure — the one nobody notices until Friday's report is wrong. In this session, we break down five AI agents that are changing how data teams operate: from monitoring pipelines 24/7 and catching schema drift at ingestion, to closing the gap between a production failure and its root cause in minutes. We'll walk through real implementation patterns, including a baseline-learning monitoring agent and a tool-use driven incident response loop, and discuss what the shift to agentic data engineering actually means for the way teams are built and how engineers grow. Whether you're evaluating agents for your platform or already running them in production, you'll leave with concrete patterns you can apply immediately.

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Agenda facts

Format: Talk / Presentation
Kind: Conference Session
Topic: Artificial Intelligence and Machine Learning
Level: Intermediate
Time: Oct 30, 2026, 10:30 AM
Room: Room F
Duration: 60 min
Public page: https://www.mitechcon.net/sessions/agentic-loops-in-the-data-stack-from-pipeline-failure-to-auto-remediation/

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Varun Joshi

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Pipeline failures should not wait for someone to notice a bad report. Every data engineer knows the 2 AM pipeline failure — the one nobody notices until Friday's report is wrong. "Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation" turns that scenario into a session where Varun Joshi walks through agentic patterns for monitoring, root cause analysis, and auto-remediation. 📅 October 28–30, 2026 📍 Oakland University, Rochester, MI 🔗 https://www.mitechcon.net/sessions/agentic-loops-in-the-data-stack-from-pipeline-failure-to-auto-remediation/?utm_source=partner-kit&utm_medium=partner&utm_campaign=mitechcon-2026&utm_content=session-agentic-loops-in-the-data-stack-from-pipeline-failure-to-auto-remediation #MITechCon #DataEngineering #DataPipelines #Observability

MediumUpdated 2026-07-08

Pipeline failures should not wait for someone to notice a bad report. Learn agentic patterns for monitoring, root cause analysis, and auto-remediation. https://www.mitechcon.net/sessions/agentic-loops-in-the-data-stack-from-pipeline-failure-to-auto-remediation/?utm_source=partner-kit&utm_medium=partner&utm_campaign=mitechcon-2026&utm_content=session-agentic-loops-in-the-data-stack-from-pipeline-failure-to-auto-remediation #MITechCon #DataEngineering

ShortUpdated 2026-07-08

Feature this MITechCon 2026 session in your newsletter or email: "Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation". Pipeline failures should not wait for someone to notice a bad report. Every data engineer knows the 2 AM pipeline failure — the one nobody notices until Friday's report is wrong. "Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation" turns that scenario into a session where Varun Joshi walks through agentic patterns for monitoring, root cause analysis, and auto-remediation. Format: Conference Session. Topic: Artificial Intelligence and Machine Learning. Level: Intermediate. Scheduled for Oct 30, 2026, 10:30 AM in Room F. Learn more and share the session: https://www.mitechcon.net/sessions/agentic-loops-in-the-data-stack-from-pipeline-failure-to-auto-remediation/?utm_source=partner-kit&utm_medium=partner&utm_campaign=mitechcon-2026&utm_content=session-agentic-loops-in-the-data-stack-from-pipeline-failure-to-auto-remediation

MediumUpdated 2026-07-08

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