Sandeep Shivam is a senior product leader with over 20 years of experience scaling AI-powered SaaS platforms across mortgage, lending, core banking, and capital markets. He currently leads Tavant's Touchless Lending Experience, an AI-powered, AWS-native product suite adopted by leading U.S. mortgage lenders, where he owns product vision, P&L, and go-to-market. An award-winning innovator recognized by HousingWire, the Mortgage Bankers Association, and Product That Counts, he is an IEEE Senior Member, a BCS Fellow, a Chartered Manager (FCMI), and a Forbes Technology Council member. He is a frequent keynote speaker at MBA Annual, HousingWire, and Digital Mortgage. His work focuses on taking agentic AI from prototype to dependable, governed production systems.

Agentic AI has moved past the demo stage. Systems that can reason, plan, and take action on their own are now entering production, and that shift raises a hard question for every product and engineering team: how do you let an autonomous agent act on real decisions while keeping that action safe, accountable, and trustworthy? This session is a practical playbook for deploying agentic AI in production, drawn from real fintech experience building autonomous systems under strict safety, compliance, and scalability requirements. Rather than treating agents as clever code, it frames them as a new kind of coworker that must be onboarded, supervised, and earned into trust over time. Attendees will walk away with a concrete deployment path. The talk covers a risk-tiered model for classifying agent decisions by impact; the control foundation autonomous agents require, including agent identity, capability boundaries, immutable audit trails, and explainability; and a graduated-autonomy ladder that promotes an agent from shadow mode to supervised, semi-autonomous, and finally autonomous operation only as it proves itself against human decisions. It also examines production safety controls such as circuit breakers, drift detection, and kill switches. The final section turns to architecture, showing why a composed multi-model ensemble outperforms a single larger model, with production results of roughly 40% latency reduction and task success improving from 91% to 97%. The session closes with a practical checklist teams can apply immediately: what to inventory, what to instrument, and what not to ship without.