Abstrae
The platform

A guarantee on every output your agents produce.

Assemble small, composable blocks into a tree that performs a task — while quantifying, with calibrated probability, that its outputs meet your specifications.

Move Cube · activity graph
Move Cube / Activity graph
Graph Timeline Logs
Live 10 nodes · 3 levels
Move Object
MANAGER·3 subtasks
Grab Object
MANAGER·done·0.9s
Get Object
WORKER·running
Place Object
MANAGER·queued
Find Grab Pose
WORKER · 0.3s
Go To
WORKER · 0.5s
Grab
WORKER · 0.4s
Find Place Pose
WORKER · queued
Go To
WORKER · queued
Release
WORKER · queued
INSPECTORWORKER
Get Object
StatusRunning
Confidence0.85 ±0.06
Elapsed1.2s
Done Running Queued
Building blocks

Four ideas, composed into reliable autonomy

01 · Activities

Workers and managers

Workers run actual code; managers wire child activities together and impose demands on them. Compose them into a tree that performs your task end to end.

02 · Causal models

Physics-aware structure

Each activity carries a small model that turns realized measurements into a belief about an outcome — encoding why things happen, not just that they correlate.

03 · Specifications

Demands with a threshold

A specification is a condition on an output plus a confidence threshold — the same step can be demanded loosely in one context and strictly in another.

04 · Confidence gating

Act only when sure enough

At runtime a step passes only when its calibrated confidence clears the demanded threshold. What can't be guaranteed is surfaced rather than silently risked.

The flow

From expert judgment to autonomous action

1

Capture knowledge

Encode expertise as a causal model of specs, problems and signals.

2

Build agents

Compose workers and managers into a tree that performs the task.

3

Learn from experience

Calibrate against ground truth with Bayesian, ML and RL methods.

4

Detect, decide, improve

Gate decisions on confidence, act when sure, and sharpen over time.

Runtime

See every decision as it happens

The control center traces specs, problems and the live scene step by step — so you always know what an agent believed, and why it acted.

‹ Config Move Object RUNNING 10.1s 1 / 3 steps 0.5× 1× 2× 4× ▶ Start Pause Restart
CONTROL CENTER
Mission 10 activities · live
Move ObjectMANAGER
Grab ObjectMANAGER
Find Grab PoseWORKER
Go ToWORKER
GrabWORKER
Place ObjectMANAGER
Find Place PoseWORKER
Go ToWORKER
ReleaseWORKER
Get ObjectWORKER
Queued Running Done Failed
Find Grab Pose — Worker Done · snapshot
4 specs · 13 problems
Plan
✓ Find Grab Pose
4/4 pass
→
Go To
0/2 pass
→
Grab
0/2 pass
Specifications 4
Object Inside Grip AreaPASS
P 0.85 ±0.07
0.85≥ 0.50
Simulated Contact AreaPASS
P 0.81 ±0.13
0.81≥ 0.50
Single Object GrabbedPASS
P 0.84 ±0.08
0.84≥ 0.50
Valid IKPASS
P 0.91 ±0.09
0.91≥ 0.50
Scene view Camera · Sensor view
Simulation scene
Problems 13
Out of range object
P 0.29 ±0.10
0.290.50
Cube too large
P 0.43 ±0.06
0.430.50
Too close
P 0.47 ±0.11
0.470.50
Too far
P 0.39 ±0.12
0.390.50
Surface too slippery
P 0.49 ±0.11
0.490.50
+ 8 more problems

See the platform on your problem

Bring a critical decision you'd like to make autonomous — we'll walk it through the platform together.

Request a demo