Choose what to delegate to an agent, how to assess its delivery, and where code should control the workflow.
Each path has its own starting point and sequence.
Decide what to delegate, check whether the requested edit happened, and separate supported claims from unanswered questions.
Start with Why I Would Delegate 3D Content Production to an Agent (Video explainer · 6:37 available) View full reading pathUnderstand the acceptance workflow, follow a delivery through feedback and repair, then decide what needs to change and who should act.
Start with Building an Acceptance Harness for Agent-Generated 3D (Video explainer · 7:02 available) View full reading pathStart with a delivered assembly, or choose a pair of articles on materials, motion or simulation. Each pair explains a check and the limits of its evidence.
Start with The 70 mm Gap Our Review Would Have Missed (Video explainer · 5:26 available) View full reading pathWhy grounding and workflow structure often matter before fine-tuning, what changed when code owned execution order, and what a safe restart still had to prove.
Start with Most Teams Reach for Fine-Tuning Too Early View full reading path1 piece not currently in a reading path.
A finding needs the right next action. These 3D deliveries taught me to separate defects and missed requirements from scope gaps, missing evidence and evaluator problems before assigning a repair. A requested variation may need no repair at all.
A delegated distribution-centre build passed every check derived from its brief. Its only automated rejection came from a delivery policy the brief never stated, and separate review found a route indicator in the wrong state between two timed events. A smaller dairy build shows the plain case: broken bindings and a local repair.
A JPEG label passed its pattern check because the comparison measured quadrant interiors and excluded the boundaries. The pass was correct, but a summary that dropped the word “interior” was not. A later detailed-brief study found wrong source pixels despite passing geometry and motion checks.
A connection check caught deliberately broken motion, while three supplied mechanisms passed its samples. Two sampling schedules each missed a defect the other caught, so the report names its sample times. A valid speed change also exposed an extra requirement in the evaluator.
A low-friction block slid 664 mm, far past a 10 mm comparison, and the delivery was still correct because the brief asked for that variant. Under the original stay-put contract, the same kind of measurement was a rejection. The harness worker first ties the result to the scene; then the brief decides what it means.
A press-shop arm changed length during its animation, making me question which production choices I was accepting. I built a harness that checks a saved delivery against its intended use and keeps failed measurements, missing evidence and pending decisions separate.
Astra built an animated crank-slider from one brief, then found the rod leaving its pin by about 70 mm between saved frames because exported angles wrapped near 180 degrees. Our planned review sampled only authored frames and would have missed it. Here is the recorded task, the fix and what I would change in the review.
My checker's dependency reader missed a texture path authored on a shader and falsely accepted three cases. That failure is why I want a missing-file control beside the correct bundle and a wrong binding, and why I treat binding, file presence, decoding and appearance as separate checks.
Changing only the USD clock rate passed a contract that checked time codes. A timing pack that checks duration and positions in elapsed seconds rejects that change and accepts a correctly rescaled animation. What happens between samples still needs its own check.
Two ramp scenes pass the selected NVIDIA USD physics-configuration rules, but only one keeps its block within a 10 mm limit. A press-cell demo passed 24 general rules while its PhysX run missed a 10 mm tracking requirement. The walkthrough shows the inputs, traces, solver sensitivity and the friction that stayed assumed.
I built SplatStage around editing, export and composition workflows. Starting again, I would test delegating that production job to a runtime agent. The question is which development work that avoids, and which tools, checks and user-facing features I would still maintain.
Four controlled cases expose an incompatible pallet-move request and a checker that never tested the destination. A feasible layout can pass while leaving the original request unresolved; the code and saved scenes are available to reproduce.
Five Astra probes produced no hallucination example. One correctly left contact, mass and friction unresolved. Together with a published object-grounding failure, that result shows why spatial evaluation needs supported, contradicted and unknown outcomes.
Anchor stopped my agent from skipping declared steps. Ratchet came from the next question: after an interrupted or overlapping run, could I prove what happened and restart without making it worse?
A technical look at the small execution runtime I built for overlapping and interrupted agent workflows: ownership, effect recovery, verified completion and the limits of a local alpha.
A repeatable agent workflow kept skipping different steps on different runs. The fix wasn't a stronger prompt; it was moving the plan into a DAG and letting deterministic code control sequence and verification.
Before changing model weights, I would identify the gap: missing domain knowledge, unreliable workflow behavior or a deployment constraint. A pharma recipe project, an agent workflow and an edge counterexample lead to different first interventions.
MES was never really about running the machines. It was about improving the operation, and it was limited by how much had to be modeled by hand and decided by people. Physical and agentic AI can change that, but only if the plant's systems are joined through shared operational context.