Inside the Control Plane

The Overview dashboard consolidates live grid pricing, workload posture, and infrastructure health across regions into a single operational surface. Operators gain immediate visibility into cost exposure, carbon intensity, and system status through a unified control interface.

This is the primary command layer of the HexaGrid control plane.

The Reinforcement Learning Agent continuously evaluates workload timing and routing decisions based on forecasted price movement, carbon intensity, and real-time infrastructure state. Using policy-based reinforcement learning, HexaGrid dynamically adjusts scheduling strategies to optimize for cost, carbon, or blended objectives.

This layer enables adaptive deferral and region-aware execution under changing market conditions.

Carbon intensity is modeled as a time-dependent operational variable, not a static compliance metric. HexaGrid integrates live grid carbon signals directly into its optimization framework, enabling dynamic cost–carbon tradeoffs across regions and execution windows.

This allows measurable Scope 2 optimization without manual intervention or performance compromise.

The Fleet layer coordinates workloads across distributed sites — including East Coast, Mid-Atlantic, Texas, and California grid regions. By continuously scoring regions on price, carbon intensity, and availability, HexaGrid routes workloads to the optimal execution environment in real time.

Geographic diversity becomes a controllable optimization asset rather than a static deployment constraint.

Optimization decisions incorporate live GPU telemetry, including power draw, utilization, thermals, and anomaly detection. This closed-loop integration ensures scheduling logic remains aligned with hardware health, stability, and capacity constraints.

Infrastructure optimization without hardware awareness is incomplete — HexaGrid closes the loop.