Multi-Agent AI Systems for Enterprise Tax Compliance: From Prototype to Production
Date to be announced · Time to be announced
Enterprise AI moves beyond experimentation when systems can operate reliably under complex business constraints. Corporate tax compliance presents a compelling example, where rules evolve continuously, calculations span interconnected schedules and jurisdictions, and every automated decision must remain explainable and verifiable. This session explores practical principles for designing multi-agent AI systems that can move from prototype to production in high-stakes enterprise environments. Drawing from experience architecting a production corporate tax preparation system, the session examines how specialized AI agents can divide complex workflows across data intake, tax determination, validation, and return assembly. It demonstrates why explicit agent responsibilities, contract-driven communication, bounded autonomy, and failure containment are essential when building intelligent systems that cannot rely solely on a general-purpose model. The session also explores practical approaches to coordination, evaluation, observability, and auditability, showing how these capabilities can be embedded into an AI architecture rather than added after deployment. Attendees will gain reusable architectural patterns for building trustworthy multi-agent applications, understand common failure modes when moving agentic systems into production, and learn how to design AI solutions that balance autonomy, reliability, transparency, and human accountability in enterprise scenarios where correctness matters. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 12:00–12:30. Timezone requires verification.
Session status: scheduled
Room: MPR A
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Speakers and their event affiliations
- Prashant Devgade
Affiliation at this event: Deloitte
Parent event
In person · Microsoft Toronto, Toronto, Ontario, Canada
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