Artificial Intelligence AI Agents—The Breakthrough in the Manufacturing Industry?

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Autonomous AI agents can make decisions independently and optimize processes. For which tasks does this work? And what can the agents achieve when they are linked into networks?

Artificial intelligence has already reached many industries, but its potential in manufacturing is still in its early stages. While AI-supported process automation and predictive maintenance are already in use in many companies, so-called AI agents can play an even greater role in the future. These systems are capable of making independent decisions and autonomously executing control processes without human interaction. Especially in dynamic production environments where unpredictable disruptions can occur, AI agents offer great potential to actively support processes, reduce errors, and increase efficiency.

Despite the enormous technological progress, many companies are still uncertain about how ready AI agents actually are for deployment. The questions that arise are: How capable and reliable are AI agents today? Where can they provide real added value? And what regulatory and technological hurdles must companies overcome to successfully integrate them into their manufacturing processes?

Seamless Monitoring Through AI Agents

One of the most promising applications for AI agents in manufacturing is the autonomous control and so-called next-step decisions based on machine data. In many production facilities, this process is still largely manual: employees detect disruptions, identify sources of errors, and decide what measures need to be taken to resolve them. This is time-consuming and requires experienced and qualified personnel.

AI agents can optimize this entire process by continuously accessing centrally stored sensor data from machines and systems and immediately detecting deviations from standard values. An AI agent can therefore not only analyze the current state of a machine but also predict based on historical data when a failure is likely. This would make maintenance significantly more efficient, as repairs or spare parts can be scheduled exactly when they are really needed—and not only after damage has already occurred.

Another advantage: While traditional monitoring systems usually only send alerts when a problem is detected, AI agents can directly provide recommendations or even automatically initiate actions. For example, an AI agent that detects an impending overheating of a machine can independently trigger or initiate appropriate measures and actions in a timely manner.

More Precise Predictive Maintenance through AI Agents

Predictive maintenance is already established in many companies, but with the use of AI agents, these systems can operate even more precisely and independently. Instead of relying solely on simple warning signals or statistical models, AI agents can analyze a variety of data sources in real-time and identify patterns that indicate future problems.

A significant advantage is that AI agents can evaluate technical data from machines and also consider external factors such as temperature, humidity, or production cycles. For instance, an AI agent can determine that a machine wears out faster in particularly humid environments than under normal conditions—and based on this, create more precise maintenance plans.

However, there are also challenges: for AI agents to function reliably, they need large amounts of high-quality data. Many companies do not yet have the necessary infrastructure to capture this data in real-time and make it usable for AI. This is where modern platforms like the AI Agent Orchestrator from Servicenow come into play, which aggregate various data sources and enable centralized control.

New Applications for AI Agents in Production

AI agents can take on an even more comprehensive role in the future. An exciting development is the use of multiple specialized AI agents that form a network and take on different tasks within a production process.

The AI Agent Orchestrator demonstrates how such coordination can work. Instead of a single AI agent handling all tasks, there are specialized agents that focus on different aspects of production. For example, one agent may concentrate on quality control, while another is responsible for enhancing the efficiency of the production line. A superior orchestrator agent ensures that all sub-agents work together efficiently.

A vivid image for this concept is an airport: the orchestrator agent takes on the role of the air traffic controller, while the specialized agents represent individual airplanes. Each agent has its own task—whether it's coordinating runway usage, managing luggage, or directing aircraft on the tarmac. This distributed AI architecture could enable a completely new dimension of automation in manufacturing.

Compliance, transparency, and user-friendliness

A crucial aspect of deploying AI agents in industry is compliance with regulatory requirements. In highly regulated sectors—such as the pharmaceutical or aerospace industries—all production steps must be precisely documented to ensure compliance with regulations. AI agents can play a key role here by automating processes and ensuring complete transparency and traceability. Special compliance agents ensure that all decisions are recorded in a traceable manner and that regulatory requirements are met, allowing companies to remain audit-ready at all times.

At the same time, user-friendliness is crucial for the widespread use of these technologies. With platforms like ServiceNow's AI Agent Studio, companies can develop their own AI agents without deep programming knowledge and adapt them to their specific requirements.

Actively Shape the Change

The implementation of AI agents often meets resistance within the workforce, as new technologies tend to trigger skepticism and uncertainty. Therefore, it is crucial to create transparency and involve employees early in the transition to foster acceptance and trust. Many employees fear that automated systems could threaten their jobs. To counteract these fears, a gradual introduction is recommended, initially automating simple, repetitive tasks. As employees realize that AI agents noticeably relieve them in their daily work, trust in the technology grows, and the level of automation can be gradually increased.

In the long term, AI agents in manufacturing can not only provide support but also take over entire production and maintenance processes as independent software services. The central challenge will be to find a balance between comprehensive automation and the necessary human control.

*Jürgen Schön works as Senior Director Manufacturing Industry at Servicenow and is responsible for the European go-to-market for digital workflow solutions in the industry.

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