Agent Cognitive Stack — 5-Layer Architecture
L0 (First Principles) → L4 (Applications). The L2 Equipment Agent Node is the cognitive core. L3 Multi-Agent Swarm enables cross-equipment closed-loop optimization.
Equipment Agents · Autonomous Physics AI
Embed thermodynamics, fluid mechanics, and mass-transfer kinetics into edge-compute nodes for physics-guaranteed perception, real-time reasoning, and closed-loop optimal control.
LLMs can't understand physics. Traditional simulation lacks autonomy. Industrial assets need closed-loop cognitive nodes.
Statistical token models with no conservation constraints. Cannot solve continuous PDEs/ODEs. Inference latency incompatible with millisecond control.
Traditional simulation is offline, open-loop, single-shot. Even when CFD accuracy is sufficient, it requires manual input → manual interpretation → manual execution. No closed-loop, no autonomy.
Closed-loop autonomous cognitive nodes: physics constraints + online learning + safety guarantees + multi-node coordination.
L0 (First Principles) → L4 (Applications). The L2 Equipment Agent Node is the cognitive core. L3 Multi-Agent Swarm enables cross-equipment closed-loop optimization.
Core Technology
Autonomous cyber-physical cognitive node. Embeds first principles into edge compute for closed-loop Perceive-Reason-Plan-Act.
Sensor Telemetry
PLC/DCS · 1 Hz
Physics + ML · Closed-Loop Cognition
Autonomous Commands
→ PLC/DCS
Multi-modal sensor fusion. PINNs infer unmeasurable internal states (soft sensing).
Embeds conservation laws into loss functions. Inverse-identifies key physical parameters for explainable diagnostics.
MPC / Safe RL within CBF safety envelope. Optimal trajectories with zero catastrophic risk.
Direct output to PLC/DCS — no human in the loop. P2P multi-agent coordination for plant-wide optimization.
Rigorously benchmarked against classic deep learning and hybrid AI on complex industrial data.
+700%
From failed models to highly predictive assets on unseen test data.
-52%
High-fidelity prediction of entire process curves using only the first 5-10% of data.
Classic AI Models
ThinkMachine EA
White: ground truth · Color: model prediction · Real-time animation
Agent Library
First-principles Equipment Agents pre-trained on simulation data. Ready to compose, fine-tune, and deploy to the edge.
Equipment Agents autonomously execute across four industrial scenarios, upgrading offline analysis to real-time closed-loop control.
We invite industry partners — production managers, process experts, and site engineers — to validate Equipment Agent closed-loop control on real production lines.
The complexity of the physical world is no longer a barrier to innovation.
Schedule Meeting →