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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16723\nAgentic AI system
 s are making decisions, routing workflows, and taking actions autonomously 
 across enterprise environments, but most organizations' data governance
  frameworks were never designed for this. Traditional governance assumes hu
 man-in-the-loop oversight, static data assets, and predictable pipelines. A
 utonomous agents break all three assumptions simultaneously. At Walmart Glo
 bal Tech, deploying multi-agent document routing systems and RAG-powered au
 tomation pipelines exposed a fundamental gap: the governance policies, data
  quality standards, and accountability structures that worked for conventio
 nal AI simply don't transfer to agentic systems.\n\nThis session presen
 ts a first-hand framework for extending enterprise data governance to cover
  agentic AI, including how to govern the context agents consume, establish 
 accountability when agents act autonomously, and build auditability into sy
 stems designed to operate without human intervention.\n\nAttendees will lea
 ve with:\n\n
 A practical framework for extending existing data governance policies to ag
 entic AI systems\n
 Strategies for governing the context and data pipelines that agents rely on
 \n
 Methods for establishing accountability and auditability in autonomous AI w
 orkflows\n
 Lessons learned from deploying multi-agent systems at Fortune 500 scale\n
 A roadmap for identifying governance gaps before agents reach production\n
 Key metrics for measuring governance effectiveness in agentic environments\
 n\n
DTSTART:20261117T154500
SUMMARY:Who Governs the Agents? Extending Data Governance Frameworks to Age
 ntic AI Systems
DTEND:20261117T162959
LOCATION: See Description
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