Just a few years back, factories were completely driven by command-and-control systems with humans in charge end-to-end. Today, in the era of smart factories, Agentic AI in industrial processes is emerging as the next leap, powered by highly interconnected systems and streaming real-time data. Machines have now acquired the eyes and ears needed to learn and act on their own. Now, imagine these systems, acquiring a mind of their own! Machines, will then be in a position to set goals, interact amongst themselves, and agree upon a workflow autonomously. With Agentic AI we are set to see this translate into a reality.
Simply put, Agentic AI takes data processing a step beyond – from generating actionable insights to independently acting upon them. It owes its name to the idea of an agent that perceives and interprets developments, plan a course of action based on self-determined objectives, and acts autonomously. Within industrial ecosystems, it takes up the role of a missing management layer, capable of setting goals, overcoming hurdles, and building dynamic workflows to meet the goals.
Smart Factories and the Role of Agentic AI in Industrial Processes
Machines Stop Waiting for Orders
It’s like a traditional assembly-line machine getting proactive. This means a machine entrusted with the task of fastening bolts will no longer restrict itself to the task. Agentic AI will make it assume the role of a floor supervisor, monitoring the flow of materials, identifying delays in advance, and doing everything necessary to keep production moving. What’s more, it would run its own calculation to determine the most effective way to meet targets – schedule shift, over time or reallocating resources.
Example
Fanuc’s Automated Production Line
Fanuc, a Japanese robotics company, runs a plant where robots are used to build robots. The plant is capable of producing robots without any supervision for 30 days with AI agents acting as supervisor managing workflows end-to-end.
Machines Become Economic Actors
When machines start acting on their own— they also become independent economic actors, commanding their own portfolios and staying fully mindful of managing their budget. Future smart machines would manage their energy consumption, accurately estimate available machine time, and reserve adequate maintenance windows. The objective would be to optimize performance with available capital to realize shared factory goals. It would somewhat be like factories being driven by a network of small self-regulating economic units.
Maintenance Becomes a Multi-agent Strategy
If smart analytics enable predicting machine failures, Agentic-AI will turn factory maintenance into a multi-party negotiation. It would be like machines rescheduling work amongst themselves to balance workload and minimize disruption. For instance, a packaging robot approaching its wear threshold might delegate its heaviest tasks to the next-in-line while preparing for maintenance slot scheduled for a later date. This coordination can extend to adapt to sudden shocks such as supplier delay to sudden demand surge, rise in energy prices etc. So, future smart factories will witness cross-line maintenance diplomacy in action to eliminate downtime.
Example
Siemens Shows the Way
While a full-agentic marketplace is still a distant cry, Siemens is among the early adopters of Agentic AI to enable predictive maintenance. This empowers its machines to act autonomously as negotiating agents, interpreting real-time data to anticipate and avert possible downtime. It is reported that the company has successfully reduced unplanned downtime by around 25%. This achievement indicates we are moving fast towards the era of cross-line maintenance diplomacy.
The Black Box of Trust
In an agent-driven factory age, algorithm-based trust will take over as the operational currency. This means if a system reroutes work or delays a job, it will justify why — such as to bypass an energy spike, circumvent a bottleneck, etc. As a result, decisions taken by algorithmic not be opaque – but understandable and auditable. Therefore, future smart factories will comprise black boxes that will log in every data input, decision, trade-off, etc.
Machines Become Peers
For humans, Agentic AI will elevate machines to peers. In the new work order, humans will work as mentors, fine-tuning the objectives of the autonomous units and may be, providing contextual judgment that is beyond the capability of algorithms. This will mark a shift in floor culture, perhaps leading to the creation of hybrid decision boards where human managers and AI agents vote to decide on production strategies.
When Agentic AI takes over, its reach will extend across factory floors to seamlessly coordinate inter-factory operations. This means, in the future, factory AI agents in different plants will talk to each other to balance production loads or overcome disruptions and maintain output with unthinkable precision. When this happens plants will enter into mutually beneficial agreements on their own driving efficiency to never before heights.
With the development of even more secure communication channels and robust IT-OT cybersecurity, different plants across different organizations may partner for mutual gains. As and when this happens, we will be in the cusp of a self-orchestrating industrial network, ready to support each other for optimized collective performance.
AI Agents in Industrial Processes
Agentic AI is set to reshape factories by moving beyond automation into autonomous decision-making.
- Quality Control: On a packaging line, an agent could spot a defect in a yogurt cup, stop the conveyor, and redirect the batch for inspection, without waiting for a supervisor.
- Supply Chain: If a shipment of plastic granules is delayed, an agent could automatically reassign orders to another supplier and update production schedules in real time.
- Maintenance: Instead of just predicting machine wear, an agent could order spare parts, schedule downtime, and dispatch a drone to inspect the equipment.
When these agents collaborate—production adjusting to supply chain changes, while energy agents optimize power use—Agentic AI in industrial processes creates a self-regulating factory ecosystem that boosts efficiency and resilience.
The promise is efficiency and resilience, while the challenge is ensuring agent decisions remain safe, transparent, and aligned with human oversight.
Agentic AI Is Inevitable—But Not Immediate
Agentic AI is rapidly evolving, but full-scale deployment is still some distance away. While the promise is enormous, the technology continues to grapple with several concept-level challenges that lack concrete engineering solutions. Among them are goal misalignment between agents that can lead to unintended consequences, the risk of agents interpreting inputs in unpredictable ways, and the urgent need for governance frameworks to ensure decision-making remains safe and fair.
At the same time, the early signs are unmistakable: Agentic AI in industrial processes is on its way. From semi-agentic systems spotting defects in real time to agents intervening in processes—and even creating other agents—the foundation for broader adoption is already being laid. When this shift gains momentum, the architecture of factory floors will transform permanently.
Comidor’s Role in the Agentic AI Era
While Agentic AI promises to revolutionize industrial processes, its success depends on seamless orchestration between data, workflows, and human oversight. This is where Comidor provides a critical foundation.
Unified Process Management: Comidor integrates business and industrial workflows, creating the digital backbone where agentic systems can operate.
AI-Driven Insights: With built-in AI and data analytics, Comidor ensures agents are not acting in isolation but are guided by enterprise-wide intelligence.
Governance and Control: Through role-based access, audit trails, and ISO-aligned compliance, Comidor provides the governance layer that keeps agentic decision-making safe and transparent.
Scalability: Whether it’s coordinating a few semi-autonomous quality control agents or managing a network of supply chain and production agents, Comidor scales with the complexity of the operation.
In short, Comidor is not just enabling the deployment of agentic AI—it’s ensuring that when factories become self-optimizing ecosystems, they remain aligned with strategic business goals and regulatory requirements. Discover how Comidor can help your organization unlock the full potential of Agentic AI while staying in control.
