Most enterprise AI deployments share a common flaw: they're only as current as the last time someone manually updated them. Intent drifts, edge cases multiply, and the teams AI was meant to free up end up spending more time maintaining it than using it.

Self-learning AI Agents work differently. Every resolved ticket, every escalation, every successful routing decision feeds back into the system, making it more accurate over time without requiring human intervention to trigger that improvement. 
 
In this session, we break down the architecture behind it and show what it looks like running in production.

What you'll walk away with:

• How to architect AI Agents that close their own feedback loops — no manual intervention required.
• The role of dynamic policy reinforcement in keeping AI Agents accurate as intent patterns shift.
• How to scale AI autonomously while keeping humans in control of outcomes.
• Real performance benchmarks: what containment lift, FCR improvement, and cost-to-serve reduction actually look like in production.