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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16692\nEnterprise AI age
 nts do not fail only because a model is wrong; they fail when ownership, ac
 cess, context, review, and evidence are unclear. This session shares practi
 tioner patterns from building production AI workflows for finance and enter
 prise operations at OpenAI: governed tool contracts, MCP-style integrations
 , Apps SDK UI surfaces, Temporal workflows, evals, citations, access contro
 ls, and human-in-the-loop review. Attendees will see how to design agent wo
 rkflows that preserve audit trails, support reviewer trust, enforce permiss
 ion boundaries, and give governance, risk, and operations teams evidence th
 ey can actually use.\n\nKey takeaways:\n\n
 Define governed tools and data contracts for agents.\n
 Separate model reasoning from permissioned actions.\n
 Use citations, provenance, and workflow state to create an audit trail.\n
 Design reviewer experiences for approvals, exceptions, and escalation.\n
 Measure agent quality through evals, incident review, and workflow outcomes
 .\n\n
DTSTART:20261118T143000
SUMMARY:Auditable AI Agents: Governance Patterns for Enterprise Workflows
DTEND:20261118T151459
LOCATION: See Description
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