AI Agents on the Factory Floor: Moving From Copilots to Closed-Loop Decision-Making

From Advice to Action

Copilots keep people responsible for both decisions and execution, while an agent is built around an outcome and may interact with other agents, applications and operational systems to reach it.

“Agents can perceive conditions, reason over context, execute approved workflows, interact with other agents, applications and operational systems, and verify results,” Ford says. “The role of the human shifts from task execution to supervision and exception management.”

Agents can handle high-volume activities governed by established procedures, while human operators monitor performance and intervene when conditions exceed an agent’s authority.

“In industrial environments, the most effective model is not humans versus AI, but human-autonomy teaming,” Ford says. “AI agents execute routine and repetitive activities, while operators retain oversight, intervention authority and responsibility for exceptional situations.”

DIVE DEEPER: Find out how to manage the convergence of IT and operational technology securely.

Start With Bounded Workflows

The strongest early use cases are predictable processes supported by extensive operational data. Ford identifies sensor malfunctions, routine startup and shutdown procedures, weather-related responses, field communications, shift handovers, logbook updates and process optimization as potential applications.

“The best candidates for autonomous AI agents are workflows that are repetitive, data-rich and governed by well-defined procedures, and that have clearly understood outcomes,” he says.

“Humans should remain in the loop for activities involving significant ambiguity, safety-critical decisions, operational envelope changes, regulatory compliance, emergency response, and novel situations where historical data may not provide sufficient context,” Ford says.

Guardrails Around Autonomy

The actions of these industrial agents must remain inside deterministic boundaries established through engineering rules, equipment constraints, safe operating windows and approved procedures.

“While agents may use generative AI and probabilistic reasoning to analyze situations, any operational action must remain constrained by deterministic rules,” Ford says.

Cybersecurity is another essential control. Agents interacting with operational technology require tightly managed access, network segmentation, continuous monitoring and system validation.

Ford explains that before receiving production authority, agents should be tested using digital twins, training simulators and other controlled environments.

Formal governance should address accountability, compliance, model management and continuous lifecycle monitoring, and should define approval thresholds.

For example, a low-risk logbook update may be automated, while a change affecting a critical production process could require explicit human authorization.

These safeguards help manufacturers distinguish genuine autonomy from “agentwashing,” in which conventional analytics or copilots are relabeled as agents.

“A meaningful autonomous system must be able to perceive, reason, execute and continuously improve,” Ford says. “If it only provides recommendations, it is an adviser, not an agent.”

READ MORE: How AI is helping manufacturers adopt a unified approach to IT and operational technology.

Measure the Operational Result

Manufacturers should evaluate agents against production using metrics such as reductions in unscheduled downtime, improved asset availability, higher throughput, greater yield and better capacity utilization.

Ford says IT and operations leaders should also track how agents affect human performance. Relevant measures include response times, operator span of control, shutdowns caused by human error and variability between shifts.

The goal is to determine the appropriate level of autonomy for each workflow, supported by human authority and evidence that the system is improving operations.

Ford notes the future of industrial operations lies in combining human expertise with autonomous intelligence.

“Successful organizations will be those that use AI agents to automate routine decisions, while empowering people to focus on the complex, high-value decisions that drive safety, reliability, and business performance,” he says.