AI in industrial environments has been shifting its role: from a copilot that guides and assists operators and plant managers, to an agent capable of making autonomous decisions and learning independently.

This article explores that transition, the key differences, and how combining copilot and agent roles can maximize productivity and innovation.

Copilot: the AI that supports

Over the last decade, Copilot AI has become a valuable assistance tool that, among other functions, alerts about deviations, analyzes data predictively, and suggests process adjustments. But—this is essential—the critical action still remains in human hands.

For example, an industrial copilot in a pharmaceutical plant may recommend changes to reactor temperature, adjust assembly line speed, or warn about supply chain delays—always awaiting human confirmation. This approach reduces errors while maintaining human control, which is essential in regulated industries such as pharmaceuticals, food, or chemicals.

Agentic AI: the AI that decides and acts

Starting in 2024, Agentic AI systems emerged, capable of acting autonomously, learning from experience, and adapting to changes in production, logistics, and supply chains—shifting from a support role to a decisive one in industrial operations.

In industry, its applications are concrete:

• Monitoring plant data to ensure parameters remain within desired ranges.

• Correcting deviations in production by adjusting machinery or processes in real time.

• Escalating incidents automatically, notifying the right teams, and triggering workflows without human intervention.

For instance, an agent can optimize production flows, schedule predictive maintenance, and redistribute resources according to demand—driving greater efficiency and reducing operational errors.

Challenges and requirements in factories

Adopting Agentic AI requires factories to have proper technological infrastructure, strong security protocols, and trained personnel.

But only by combining system autonomy with human oversight can its benefits be fully realized without compromising safety or operational reliability.

Combining copilot and agent

Maximizing productivity requires well-defined roles: the copilot guides and supervises, while the agent executes tasks autonomously and learns from experience. Some agent decisions require human validation, while others are carried out automatically.

For example, in a plant, an agent may reprogram production based on demand, while the copilot alerts about deviations or bottlenecks—improving efficiency without compromising human control.

Key benefits of the combination

• Automates repetitive or complex tasks without losing oversight.

• Enables continuous learning by leveraging copilot feedback.

• Maintains human control in critical decisions, essential in regulated industries.

Why the evolution from copilot to agent matters

Shifting from copilot to agent enhances human decision-making, turning AI into an active partner that analyzes data, anticipates problems, and optimizes operations.

In critical industries such as pharmaceuticals, food, chemicals, or advanced manufacturing, this evolution provides greater agility, autonomy, efficiency, and regulatory compliance—allowing early adopters to set the pace in their sector.