Making AI Agents Trustable, Secure, and Self-Learning

At-scale AI agents are the future of enterprise IT, but largely are still stuck in project mode rather than being deployed into production systems. We’re not just talking about savvy individuals augmenting their work with agentic tools. We’re talking about thousands (or more) of agents deployed inside large organizations, augmenting humans, interacting with each other, and automating processes ranging from back-office analytics to core product engineering. But delivering on this vision will require reimagining enterprise security and AI-model optimization.

In this webinar, VAST Data’s Jeff Denworth and Sagi Grimberg will discuss how VAST’s forthcoming PolicyEngine and TuningEngine work in tandem to create AI systems and interactions that are trusted, explainable, and continuously learning. PolicyEngine governs agentic activity and TuningEngine manages model tuning, working in conjunction to power automatic learning loops that remain aligned with organizational expectations.

• How PolicyEngine and TuningEngine further the capabilities of the VAST AI OS as a center of gravity for AI workloads.

• The intricacies of maintaining access controls, regulatory compliance, and data-security policies when deploying large teams of AI agents.

• The promise of automated reinforcement learning, fine tuning, and continuous learning at the organizational layer, resulting in AI models that are optimized for their specific use cases.