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Mayank
Agarwal
Director of Product Management, AI Products
RingCentral
Mayank is Director of Product Management for AI Products at RingCentral, where he leads the AI Assistant products across the company's collaboration, communication, and contact center suites, shaping how millions of enterprise users interact with AI in their daily workflows. His work sits at the intersection of agentic AI architecture and large-scale product delivery, navigating the real-world constraints of latency, reliability, cost, and trust that separate impressive demos from systems businesses actually depend on. Before RingCentral, Mayank founded an AI-first global macro trading firm where he designed and built autonomous trading systems across equities, fixed income, commodities, and currencies. There, he built multi-agent architectures where specialized agents collaborated, competed, and coordinated with each other. He also pioneered reflection agents that continuously evaluated their own strategy performance, detected signal decay, and autonomously improved without human intervention. The unforgiving feedback loop of financial markets, where poor AI decisions have immediate, measurable consequences, shaped his approach to building systems that must be not just intelligent, but self-aware and resilient.
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02 April 2026 13:45 - 14:15
Beyond the summary: AI agents that drive collaboration outcomes
Every team is building agents. Far fewer are shipping ones that reliably drive outcomes. The next frontier isn't more agents - it's agents that close the loop on real work. At RingCentral, I've scaled our AI Assistant from passive note-taker to a swarm of outcome-driving agents. Before that, at my quant trading firm, I built self-correcting agents that autonomously improved strategy performance, teaching me that real value isn't in the report, it's in what the system does next. We'll map the maturity curve from passive summarization to proactive action, walk through the agent architecture that powers outcome-driven collaboration, and confront the hard accuracy problems most teams underestimate - context collapse, intent drift, and cascading errors in multi-step workflows. Attendees will leave with a KPI framework for measuring AI agent ROI and a concrete roadmap for getting their agents from unreliable to production-ready.