VAST Forward 2026, VAST Data has announced the VAST Data PolicyEngine and VAST Data TuningEngine, 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.
Specifically, 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.
“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.”
Introducing the VAST Data PolicyEngine
AI workflows and agents are increasingly accessing organizational data, using it to produce more information in the form of generated responses, agent-to-agent communications, event logs, and more. Without fine-grained controls on what agents can access and how they communicate with other agents, tools, and remote data products, the chance for data spillage and leakage rises greatly. Without strict controls on how data is accessed and how services communicate, and without tools to log every aspect of an agentic workflow, AI cannot be fully trusted.
The VAST PolicyEngine resolves these concerns via an inline policy enforcement engine to safeguard every aspect of agentic interaction and communication. PolicyEngine governs agents’ access to shared memory, external tools, knowledge bases, 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.
Introducing the VAST Data TuningEngine
VAST AgentEngine is the agentic runtime of the AI OS. It’s a serverless computing environment that’s 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 of the system’s telemetry, as well as agent and model feedback, to support fine tuning and reinforcement learning pipelines.
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. This will kick off a new learning loop that uses future interactions to improve on the newly deployed, updated model.
TuningEngine will serve as a natural extension of VAST’s partnership with NVIDIA by integrating with NVIDIA’s NeMo Data Designer to train and fine-tune NVIDIA Nemotron open models.
A Big Step Toward VAST’s Thinking Machine Vision
These new capabilities represent a massive step toward building systems that automatically evolve as they interact with data from the natural world. VAST Data has been working on building such a system since 2016, and unveiled the full extent of its vision in 2023. With today’s announcement, VAST AI OS finally 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.
