VAST Data Creates Platform for Self-Learning Agentic AI Systems

VAST Data, the AI Operating System company, announced two new computing services that will allow the next generation of the VAST AI Operating System to deliver key requirements for organizations looking to scale their mission-critical AI initiatives: the VAST Data PolicyEngine and VAST Data TuningEngine. 

Specifically, PolicyEngine and TuningEngine work in tandem within the VAST DataEngine to create AI systems and interactions that are trusted, explainable, and continuously learning, according to the company.

PolicyEngine governs agentic activity and TuningEngine manages model tuning, working in conjunction to power automatic learning loops that remain aligned with organizational expectations.

“Just as people are always learning, so should tomorrow’s applications,” said Jeff Denworth, co-founder at VAST Data. “With the introduction of PolicyEngine and TuningEngine, the VAST AI Operating System has become a thinking machine that customers can deploy wherever they compute—a machine that safeguards every interaction and learns from every outcome, bringing the power of AI within reach of every organization.

The VAST PolicyEngine offers an inline policy enforcement engine to safeguard every aspect of agentic interaction and communication.

PolicyEngine governs agents’ access to shared memory, external tools, knowledgebases, or other agents by permitting access, actions, and communications according to fine-grained, explicit permissions, as well as AI-derived context.

Because enforcement occurs before actions execute, and because the system maintains extensive, tamper-proof traces and logs, the system maintains a zero-trust operating posture to ensure that decisions and actions remain observable, explainable, and auditable, the company said.

VAST AgentEngine is the agentic runtime of the AI OS. This serverless computing environment is simple to program and coordinates multi-agent workflows, model invocation, and agentic tool usage within the VAST AI OS.

While AgentEngine has been suitable for the deployment of static models, the completeness of the AI OS stack allows the platform to also support “learning loops” that use all the system’s telemetry, as well as agent and model feedback, to support fine tuning and reinforcement learning pipelines, said the vendor.

The VAST TuningEngine captures outcomes from agentic pipelines and utilizes curated feedback to enhance model performance over time.

Using popular methods such as LoRA fine tuning, supervised fine tuning, and reinforcement learning, TuningEngine pipelines automatically ingest that data, process it, and suggest new candidate models. Each new candidate can be evaluated and benchmarked within the VAST AI OS, and then manually or automatically deployed into the platform.

“These new capabilities represent a massive step toward building systems that automatically evolve as they interact with data from the natural world,” VAST Data said.

With this announcement, VAST AI OS creates a closed operational computing loop that observes, reasons, acts, evaluates, and improves—all while fortifying security and explainability by unifying and safeguarding all activities in one unified system, said the vendor.

The VAST PolicyEngine and TuningEngine are slated for release by the end of 2026.

For more information about this news, visit www.vastdata.com.