Personalized Experiential Learning: How Do Enterprises Build AI Agents That Learn and Adapt for Users and Tenants?

This session examines how a traditionally static enterprise agent workflow was redesigned around AI-driven adaptation. In most enterprise settings, agent behavior is defined through fixed prompts, rules, and workflows that apply broadly across users and tenants. We redesigned that model into an adaptive agent architecture built around explicit policy layers, feedback loops, and governed personalization. Structurally, this changed the system from one-time configuration to continuous learning: policies were separated from model reasoning, policy scope was organized across global, tenant, and user levels, and agent behavior was connected to both explicit and implicit feedback signals that could influence future decisions.

The session will focus on what changed in the system architecture and operating model to support this shift. It will cover how policy-driven personalization was introduced, how feedback became part of the runtime and improvement loop, and how adaptation was managed without losing enterprise control. It will also discuss the practical challenges that emerged when moving from static agents to adaptive ones, including handling conflicting user and tenant needs, keeping personalization aligned with core guardrails, and making behavior changes observable and manageable in production.

Attendees will walk away with a practical understanding of how to redesign enterprise agent workflows around adaptive AI capabilities. They will learn the core concepts behind Personalized Experiential Learning, the structural changes needed to support policy-based adaptation, and the key lessons and best practices for making adaptive agents workable in real enterprise environments. They should leave better equipped to think about how to introduce governed personalization, feedback-driven improvement, and policy evolution into their own AI agent systems.