Quali, the platform engineering and intelligent infrastructure automation company, announced a significant expansion of Torque, introducing Agentic Control-Plane capabilities designed to govern the rapidly emerging class of autonomous, GPU-intensive AI workloads. This expansion positions Torque as the control layer enterprises now require as AI systems evolve from static pipelines into adaptive, intent-driven environments where autonomous AI assistants plan, act, and reconfigure infrastructure independently.

As organizations adopt agentic AI, the familiar boundaries of infrastructure provisioning and automation are shifting. Traditional automation executes predefined workflows in response to triggers. Autonomous AI assistants operate differently. They reason continuously, take initiative, and modify infrastructure in response to changing conditions without waiting for human instruction. Torque's expanded capabilities address this shift by providing a governance layer that interprets workload intent, enforces policy in real time, and integrates seamlessly with a customer's existing cloud, infrastructure-as-code, and hybrid automation stack.

Lior Koriat, CEO of Quali, said: "AI workloads today behave less like automated jobs and more like autonomous assistants operating at machine speed. Traditional automation was never designed to govern systems that continuously adapt and make decisions on their own. Torque provides an intelligent control plane that interprets intent, enforces guardrails in real time, and governs change as it happens, while building on the automation enterprises already have. As infrastructure becomes increasingly autonomous, governance must be continuous, not static."

The newly introduced Agentic Control-Plane capabilities position Torque as a unifying governance layer that operates across infrastructure-as-code frameworks, Kubernetes ecosystems, GPU clusters, cloud platforms, and on-prem environments, coordinating both human-defined automation and machine-initiated action.

Torque brings intelligence into the orchestration process through:

• Agent-driven operations: Modular agents support blueprint creation, cost modeling, drift remediation, lifecycle management, and compliance checks, acting autonomously based on workload intent rather than static rules.
• Continuous runtime governance: Security, compliance, and financial controls are enforced throughout the full environment lifecycle, dynamically adapting as AI systems change behavior, scale, or topology.
• Seamless integration: Torque enhances customers' existing cloud and automation tooling instead of replacing it, ensuring prior investments remain intact and operational.
• Scalable infrastructure intelligence: GPU-dense and hybrid-cloud environments are governed in a way that adapts naturally as agentic AI pipelines evolve.

Torque's expanded capabilities establish a foundation for governing intelligent infrastructure as it continuously changes across clouds, environments, and workloads, bridging the gap between traditional automation and autonomous infrastructure operation.