AI Agents That Learn: How to Build Systems That Continuously Improve Themselves
Empower your teams to build AI Agents that learn from every interaction, autonomously adapt, and continuously improve without manual retraining.
RegisterEnterprises are racing to deploy AI Agents across customer service, marketing, and operations—but most systems plateau shortly after launch. Performance drops, intent patterns shift, and teams are forced into endless cycles of prompt tweaking, retraining, and manual optimization. As AI footprints grow, so does the operational burden required to maintain them.
The next wave of enterprise value will come from AI Agents that don’t stagnate—systems that learn from every interaction, adapt autonomously, and continuously improve while maintaining full governance and a human-in-the-loop.
In this session, we’ll explore how Sprinklr enables organizations to design truly self‑learning, outcome-driven AI Agents. You’ll see how autonomous feedback loops, dynamic policy reinforcement, and real-time outcome optimization allow Agents to refine routing, improve resolution paths, and evolve workflows without manual intervention or constant retuning.
Speakers include:
Georgina Wilczek
Event Host
CX Network
Sponsored by
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