Managing the Rise of Agentic AI in Manufacturing

Managing agentic AI in manufacturing requires governance built on three principles: centralized control, open standards, and traceable data infrastructure. Peter Sorowka, CEO of Cybus, outlines how manufacturers in regulated sectors like pharma and energy can address interoperability, prevent agent sprawl, and track success beyond ROI.

How Can Regulations Keep Up With Agentic AI?

Risk-based regulatory frameworks are the most practical answer for keeping pace with agentic AI in manufacturing. Rather than prescribing technological details, regulators should focus on principles such as traceability, data integrity, and auditability. Manufacturers at Cybus have demonstrated that complete data governance and traceable interfaces can satisfy compliance requirements while also producing positive economic outcomes.

The key lies in risk-based frameworks: regulators should prescribe fewer technological details and focus more on principles such as traceability, data integrity, and auditability. Manufacturers, in turn, need data frameworks and tools that enable transparency by design. We have already seen several manufacturing customers not only comply with regulatory requirements through complete data governance and traceable interfaces, but also operationalize them for a positive economic effect.

What Metrics Should Manufacturers Track for AI?

ROI alone falls short for evaluating agentic AI deployments in manufacturing because it is often too difficult to determine precisely. Manufacturers should track four additional categories: time-to-decision, compliance accuracy, employee satisfaction and acceptance, and system resilience. These qualitative factors can be measured objectively using an interoperable data infrastructure.

Time-to-decision: How much does the time from data input to action and solution decrease?

Compliance: Is regulatory accuracy maintained or even improved?

Employee satisfaction and acceptance: Does AI increase safety and reduce monotonous work?

Resilience: How quickly does the system recover from disruptions or deviations?

These qualitative factors are particularly crucial for a sustainable assessment – and can be measured objectively using an interoperable data infrastructure.

How Do You Achieve AI Agent Interoperability?

Interoperability between AI agents from different vendors is one of the main challenges in industrial AI. The practical path forward combines open, standardized data models with a neutral connectivity layer called a Unified Namespace (UNS). A UNS allows companies to connect different AI agents, maintain data governance, and avoid vendor lock-in from proprietary protocols.

The way forward is through open, standardized data models and infrastructure capable of harmonizing information from any source. Using text-based configurations helps ensure that AI agents can interpret and act on factory data consistently.

A key step is building a neutral connectivity layer, known as a Unified Namespace (UNS). This foundation allows companies to connect different AI agents easily, maintain data governance, and avoid vendor lock-in.

Why Do Open Standards Matter for Industrial AI?

Open-source ecosystems will play a decisive role in determining which standards prevail for agentic AI in industrial environments. Standards such as OPC UA and MQTT have already gained momentum through open community work. Manufacturers that rely on platforms natively supporting open-source standards retain the flexibility to integrate their own agents or connect to new ecosystems without dependency on a single vendor.

For agentic AI, this means that manufacturers benefit from relying on platforms that natively support open source standards. This allows them to retain the flexibility to integrate their own agents or connect to new ecosystems.

What Is Agent Sprawl in Manufacturing?

Agent sprawl is the uncoordinated proliferation of autonomous AI systems acting independently without proper oversight. It is a real operational risk in manufacturing environments deploying multiple AI agents. Preventing it requires three principles: centralized governance through a common data and control layer, a clear role model assigning each agent a defined function, and continuous monitoring with feedback loops to maintain control at all times.

Centralized governance: All agents must be orchestrated via a common data and control layer.

Clear role model: Each agent should have a clearly defined function instead of doing “a little bit of everything.”

Monitoring & feedback loops: Companies must maintain control at all times by continuously monitoring and controlling agents.

We have seen customers creating an integration layer for all data flows – the basis on which the manufacturers can now control their agents before chaos ensues.

How Do Manufacturers Build Trust in AI Autonomy?

Success with agentic AI in manufacturing depends on clear governance, open standards, and reliable data structures. Manufacturers that adopt these principles can move toward coordinated, intelligent, and accountable automation, where AI strengthens operations rather than adding complexity.

Manufacturers that adopt these principles will be able to move confidently toward coordinated, intelligent, and accountable automation—a model where AI strengthens operations rather than complicating them.

Related articles:

• Agentic AI in manufacturing does not need to begin with a moonshot
• Building the Data Foundations for Agentic AI in Manufacturing

For the latest 2026 deployment data including ROI benchmarks from Siemens, Honeywell, and Rockwell Automation, see Agentic AI in Manufacturing: ROI vs. Reality.