The physical AI harness
C3D Studio is the agentic harness for real-world automation. It puts inspection cells, handling cells and lab equipment behind typed calls that AI agents can use, and every actuation passes a risk class, a simulation rehearsal, an approval gate and a journal.
typed functions in one toolset
1D, 2D and 3D algorithm functions
device drivers across vendors
MCP tools, each with a risk class
Why we exist
Models are becoming commodities. What stays scarce is a safe contract that reaches real equipment, and the record of everything it did. That is what we build.
Two ideas
Everything further down builds on these two. The harness decides whether a call may run. The ontology knows what that call means in your cell.
Every device and function sits behind a typed call. Each call has a risk class, is rehearsed in simulation, is approved by a person when it has to be, and is written to a journal.
Answers: may this call run?
An agent can drive real machines without being trusted blindly.
The things in the cell, what each can do, what every action needs and changes, and the rules people carry in their heads, written down as typed data the harness can check.
Answers: does this action make sense, right now?
A wrong action is refused in milliseconds, before a rehearsal, a person or the machine.
Why both The plan asks for a scan of side B. The part is lying side A up.
R-SIDE-UP · side A is up. Nothing ran and nobody was asked. The agent re-plans with a flip.01 · The harness in detail
Stages, robots, cameras, 3D scanners, DIO and PLC links sit behind typed calls. Agents reach them over MCP and use the same calls a person would.
Each of the five risk classes decides which gates a call has to pass before anything moves. Motion limits are validated before a change is committed.
Motion runs in the simulator first. Only a passing rehearsal earns a ticket, and the gate re-derives that ticket instead of taking it on trust.
Decisions that need a person go to the approval window. The handshake fails closed, an agent cannot approve itself, and nothing else stalls while the operator decides.
Every actuation lands in the change journal and the experiment ledger. Any run can be reproduced, halted, or handed over as it stands.
In practice · XZT scanner
XZT is a tabletop side-head X/Z/T gantry: a 2 kg 3D profile sensor on a tilt head, over a Y stage and a rotary seat. Drives, controller and PC sit in a two-tier rack beside it. One Studio app drives the real machine and its simulated twin through the same calls.
The head moves in X and Z and tilts to face the part's side walls. Every axis carries a motion limit the gate checks before a move is committed.
A Y stage and a rotary seat carry the part through the sensor's line and turn it, so one pass sweeps a face and the next face comes round.
The scan path runs in the twin first: reach, clearance to the frame, cycle time. A passing rehearsal issues the ticket the real move needs.
The operator approves, the real gantry scans, and the point cloud, the measured features and the approval land in the journal together.
Industrial PC · rack
3D sensor · X / Z / T
Y stage · rotary seat
E-stop · keypad
Drives & controller
Ready-made cells
Each example is a complete app, a state chart, a form and scripts, running in the built-in robot simulator, which handles rigid parts, cloth and liquid. The agent installs one and adapts it to your spec; the same calls then drive the real cell.
An eye-in-hand depth camera tracks each block down to the grasp and sorts it by size.
A 6-axis arm stacks cartons a 3D camera measured on the belt; unstable ones wait in a buffer.
One arm holds the shoe, a 3D scanner measures the sole, a second arm lays the adhesive.
Pipette, develop, read, then plate the strongest positives and count colonies.
96-well dosing, incubation, IC50 fit and a Z′ verdict, end to end.
Stations are read from the scene, so a campaign can search for a faster layout.
A 400 × 300 mm cloth taken by one corner hangs, drags and crumples like the real thing.
174 ml tipped over a beaker lip into a 90 mm cup: 99.7 % landed in it.
Hover a card for the simulator render. Examples use simulator models of commercial robots and sensors; names belong to their owners.
Where it sits
Device control and HMI are where the revenue already is. Orchestration and provenance are where nobody is yet. Studio holds all three in one process, and plugs into the rest.
Studio doesn't pick the brain.
It gives the brain eyes, hands and a record.
Platform · Extensible by design
The runtime holds the HAL, the script engine, state charts, the gate and the journal. Everything else is a plugin, and every plugin is reached the same way: a typed call with a risk class and a journal entry.
Robots, stages, cameras, 3D scanners, DIO and PLC links load as device drivers behind the HAL's interfaces. Each has a simulated twin, so a cell runs before its hardware arrives.
90+ widgets, from charts and 3D viewers to the robot simulator and lab instruments, drop into forms in the visual designer. The agent wires them to the state chart.
33 modules of 1D, 2D and 3D algorithms: registration, matching, measurement, mesh, segmentation and local perception models. Each function is typed and searchable, so agents compose them.
Build your own device, widget or algorithm against the plugin SDK. User modules live in their own namespace, never collide with official ones, and hot-reload without restarting Studio. Mix any of them in one app.
How development runs
What the cell proved is where the next iteration starts.
Development automation · 3D algorithms
The agent searches 2,900+ typed functions, chains the ones that fit, and runs the chain live in the engine on your real scan data. Most measurements never need new code.
When a block doesn't exist, the agent stages a change set: a script or a native algorithm plugin built against the SDK. Nothing touches the running system yet.
Criteria are hashed before the first run. The candidate is measured against the baseline on recorded scans, repeated for repeatability and checked on held-out data. Evidence is pinned to the digests of source, input and output.
The verdict and its evidence go to the approval window. Approval applies the change, and Studio confirms the runtime has actually reloaded it, because applied is not reloaded.
The plugin hot-reloads on the station and ships to every processing node, so throughput grows by adding ordinary PCs. Each result records which version produced it.
Cluster mode
Switch cluster mode on and the same scripts send their heavy steps, reconstruction, matching, inspection, to ordinary PCs on the network. The station keeps its devices, its screen and its gate.
Internal cluster tests, July 2026. Larger payloads add transfer time.
02 · The ontology in detail
A risk class knows how dangerous a call is, not what is true in the cell. A fixture still holding the last part, a forgotten flip, a part heavier than the gripper is rated for: the rehearsal passes, and the mistake shows up on the machine, or never.
Things are typed classes with fields and units: a gripper with its payload, a part with its mass and the side facing up. Actions declare what they need, what they move and what they change, and are frozen by hash once a record exists, so old records keep their meaning.
Before the gate asks anyone, the plan is walked on a copy of the world. The first step that breaks a rule is refused with the rule and the fact. The plan is never quietly repaired; the agent re-plans. A 32-step plan is checked in under 3 ms.
The operator approves steps written as sentences, not joint targets. Each executed step becomes a ledger record and updates the world, so the next plan starts from what is really on the bench.
A hypothesis probed on the real machine and accepted by a person becomes a fact that blocks or relaxes later plans. Domain knowledge stops living in one person’s head: in a pilot, the rules picked the right measuring head 98% of the time against 38% without them.
Two loops, one record
Every run is typed by the ontology, checked before it moves, and written to the same ledger. Accepted evidence becomes a fact the next plan has to respect.
The guidance agents work from is improved the same way: small models run hidden tasks in a test Studio, a stronger model proposes edits, and an edit is adopted only if it wins on tasks it never saw.
Internal pilots, October and September 2026. Small samples; we publish them as they are.
Lab in the loop
State the goal and the agent proposes the next experiment: what to change, and what to watch.
Pass criteria are hashed before anything runs. Change a tolerance and it becomes a new revision; results measured against the old one stop counting instead of carrying over.
Every step that moves equipment runs in simulation first. Only a plan that passes goes to the real machine.
The equipment runs and the measurements come back, tied to the digests of the source, the input and the artifact they were taken on.
Results land in the experiment ledger and are judged against the fixed criteria. The verdict seeds the next hypothesis.
Closed loop, first class
Goal, parameter space, budget, stop rule. A script, a model or an agent picks the next run.
{
"goal": "hole position error under 20 µm",
"parameterSpace": {
"exposure_ms": [2, 12],
"gain_db": [0, 6]
},
"budget": { "runs": 40, "hours": 6 },
"stop": "3 consecutive passes",
"decide": "agent",
"criteria": "sha256:4be1…c09a"
}Capabilities
Stages, robots, cameras, scanners, DIO and PLCs behind one typed surface, each with a simulated twin.
Chain 1,200+ proven functions instead of writing new code. 11× repeatability, by composition alone.
The agent drafts chart, form and script from your spec. A person approves.
40+ robot models, IK, collisions and cycle time. A pass issues the ticket.
Scale out on commodity PCs: 36 of 36 tasks, ~10 ms overhead, lossless failover.
Any model, per role. On-premises, with a data-residency switch.
Local models for segmentation, pose, grasping and anomalies.
Journal, ledger, reproduce tool and a handover package.
Use cases
3D verdicts on mixed-vendor cells, next to your PLC.
Closed-loop campaigns with criteria fixed up front.
One gate in front of every instrument, audited by default.
Experiments that arrive with their record attached.
Why Studio
Oversight
EU AI Act and ISO 10218:2025 ask for human oversight. Studio records it as it runs.
Studio is not a safety PLC. It runs alongside your safety-rated control.
Move the scanner head to X 105 mm, Z 150 mm
→ journaled with who, when and why
Principles
FAQ
No. You pick the model; we build the contract that lets it move real equipment safely. Perception and judgement run on our own local models.
No. Studio talks to your PLC over S7, OPC UA or Modbus and owns only the agent interface and the record.
Nowhere. Studio runs on-premises, and a data-residency switch refuses any model call that would leave the site.
Talk to sales
Tell us about the equipment, the scale and the timeline, and we'll get back to you.