How it works
From expert judgment to autonomous action
1
Capture knowledge as a causal model
Encode how specifications, problems and physical signals relate — a small DAG of transforms over realized measurements.
2
Build agents from composable activities
Assemble small blocks — workers that run code, managers that wire them together — into a tree that performs the task.
3
Learn from experience
Activities self-calibrate against ground truth using Bayesian, ML and reinforcement-learning methods — sharpening confidence over time.
4
Detect, decide, improve
At runtime, agents evaluate confidence against an explicit threshold, act when they're sure enough, and surface what they can't yet guarantee.
