🤖 Before the Robot Arrives: What Can AI Agents Test in Production? 🤖

[Hasan S. Cemkan]

An automated fastening cell looks so simple in a demo. A robot picks up a tool, finds the hole, drives the fastener, and records the torque. Everything is repeatable, the cycle time is promising. But when it gets to production, things get messy. A different product variant needs another fixture, components arrive in trays out of the robot’s reach. The quality team wants proof of both torque and angle. A single operator now has to feed two stations, and the new cell spends part of the shift waiting.

Even if the robot does its job perfectly, the production decision can turn out wrong. This is where I think AI agents have a practical role in industrial automation: to prepare and interrogate an engineering decision before it becomes a purchase order. Not to control the factory, not to certify safety. To help a small engineering team discover outcomes earlier.

🤔 The Question That Precedes Equipment 🤔​

Consider a hypothetical manufacturer assembling industrial enclosures. They are considering replacing manual screw fastening with a robotic cell. The actual goal is not to install a robot; it is to increase accepted output without introducing unacceptable quality or safety risk.

An agent could start with existing work instructions, product revisions, fastening requirements, a floor plan, production records, and equipment information. It could organize the inputs and return a list of unanswered questions: Are fasteners presented in a consistent orientation? Which product variants share the same fixture? Where is inspection performed? What happens when a component is missing?

This list is not a completed analysis, it is a start. If a necessary input is missing, the agent should say so, not fill in the blank with a plausible number.

💡 Generate Alternatives, Not a Conclusion 💡​

The next useful task is to prepare options. One might redesign the manual fixture and presentation method. Another might introduce an assisted fastening tool with digital traceability. A third might use a robotic cell.

For each option, an agent could help assemble prerequisite process steps, identify interfaces impacted by the change, request missing supplier specifications, and prepare a comparison of equipment, personnel, floor space, inspection, and anticipated operating constraints. When appropriate tools are connected, it could also prepare CAD-related inputs, process documentation, or simulation configuration files for engineer review.

What happens after that is important. A geometric clearance requires a geometric check. Robot reach and cycle time require engineering models. Material flow and queueing behavior require an appropriate process simulation. Safety functions require a qualified risk assessment. The confident explanation of a language model cannot substitute for any of these.

🚧 Test the Boundary Conditions 🚧​

The most misleading outcome is often the one that looks cleanest. A virtual robot moves quickly, and the model shows improved output. But the scenario might assume ideal part delivery, no rework, a constant product mix, and an operator who is always available.

The factory does not always work that way. A reliable assessment requires observed cycle time variation, equipment downtimes, rework, material availability, and shared resource constraints. The proposed model must reproduce the relevant behavior of the current process closely enough for the decision it is asked to support.

AI agents can be useful here too. They can keep a record of assumptions, separate measured values from estimates, generate sensitivity test plans, and compare how a recommendation changes when an input changes. If a robotic cell is only beneficial when parts arrive flawlessly, that dependency should be visible before anyone approves the investment.

NIST’s work on digital twins for advanced manufacturing emphasizes verification, validation, and uncertainty quantification as necessary parts of reliable decision support. Their research on digital twin trustworthiness makes the same point: a model’s outputs should be evaluated for their intended use, rather than accepted because the model runs.

🤝 State the Hand-Off Clearly 🤝​

The line between preparation and approval is more important in a factory than it is in a slide deck. An agent can prepare an alternative process plan, flag conflicting inputs, or draft a supplier question. But it should not independently publish a robot program, change a safety-rated operating procedure, approve a machine purchase, or declare an installation ready for production.

A useful decision package should answer a few simple questions: What change is being proposed? Which inputs are observed, and which are assumed? What model or calculation was used? What could make the recommendation wrong? Who is responsible for checking? What physical experiment will validate the outcome?

After implementation, the team should compare what happened to what was predicted. Otherwise, the next simulation starts with the same blind spots.

🌱 Start Small Enough to Learn 🌱​

Manufacturers do not need a digital copy of every machine and aisle to test this approach. Start with a limited production decision. Collect real data, prepare alternatives, test engineering assumptions, and let the responsible people decide if an experiment is warranted.

I am experimenting with AI-powered factory models, CAD generation, and simulation workflows. The part that is getting faster is putting together the initial scenario. The hard part remains connecting that to the real constraints of a production operation.

That’s why I don’t think the near-term breakthrough is an AI agent that runs a factory on its own.

It’s a workflow that helps engineers discover a bad automation decision while it’s still cheap to change.