On September 8, 2026 in All, Automation, Computing, Industrial, Industrial Automation, IoT by Abhishek Jadhav

How agentic AI can coordinate industrial workflows without replacing deterministic control

Many industrial systems can identify potential problems before they disrupt production. For instance, a predictive maintenance model can detect abnormal vibration in a motor or pump. But the alert does not complete the maintenance task. Technicians still have to examine the asset, determine whether operating conditions explain the change, assess production requirements, review maintenance records, identify the necessary parts, and schedule the work.

These steps typically require people to move between processes, maintenance systems, production applications, and engineering documents. Industrial artificial intelligence (AI) can produce the initial insight, but turning that insight into a coordinated response remains a human task.

Agentic AI can help narrow this gap by coordinating the multistep work needed to move from insight to controlled action. In this first blog of our agentic AI series, we define agentic AI, examine the core technologies that enable it in industrial automation, and explain its potential importance to industrial operations.

What Is Agentic AI?

Agentic AI refers to a goal-oriented software system that can pursue an assigned objective through a continuous sequence of decisions and actions. The agent observes its operating context, interprets the objective, breaks it into smaller tasks, and determines what information or capabilities it needs.

It can then select authorized data sources, analytical models, and software tools, perform approved actions, and assess whether each step produced the expected result. This creates a workflow-level feedback cycle described as observe, interpret, plan, act, evaluate, and adapt.

For instance, consider an abnormal vibration detected by a predictive maintenance model. The agent can check the asset’s operating mode, retrieve alarms, review previous maintenance records, and determine if a work order already exists. It can then check the production schedule and the availability of spare parts before preparing a maintenance request. If the agent determines that the vibration occurred during startup or that a required part is unavailable, it can adjust the remaining workflow.

This behavior distinguishes agentic AI from other forms of automation and AI. Conventional automation follows logic and sequences. For example, a programmable logic controller repeatedly executes predefined control logic in response to process inputs. This deterministic behavior remains the appropriate choice for repeatable, time-critical machine and process control.

Agency also does not imply unrestricted autonomy in industrial operations. An industrial agent may have read-only access and provide recommendations to an engineer. It may assemble a proposed action and wait for approval, or it may execute a narrow set of preauthorized, low-consequence actions.

These limits are fundamental to an agentic AI ecosystem. The industrial agents should operate at the supervisory, analytical, or workflow coordination level. The permissions, approvals, interlocks, and established operating procedures will continue to define which agentic actions are permitted.

The appropriate objective is to maintain bounded agency to coordinate work more effectively without compromising existing controls and safety protections.

Core Technologies for Industrial Agentic AI

An industrial AI agent is an integrated architecture in which data infrastructure, analytical models, planning mechanisms, application interfaces, and operational controls are coordinated.

Each capability depends on the others. For instance, models need reliable context, plans require access to tools, tool calls must be governed, and the results of every action must be returned to the agent so it can determine the next step.

These requirements can be grouped into four interrelated capabilities: contextualized industrial data, analysis and reasoning, planning and orchestration, and governed access to industrial tools.

Contextualized Industrial Data

An agent needs an accurate representation of the environment in which it operates. Depending on the task, this representation may include sensor measurements, equipment states, operating modes, alarms, maintenance histories, production schedules, spare parts information, operating manuals, and standard procedures.

However, access to data alone does not provide the complete operational context. Open Platform Communications Unified Architecture (OPC UA) is relevant because it provides mechanisms for representing industrial information as structured objects, variables, types, relationships, events, and methods.

Earlier this year, the OPC Foundation announced plans to convert more than 430 Companion Specifications into formats optimized for retrieval-augmented generation (RAG), the Model Context Protocol (MCP), and AI-assisted engineering workflows.^[1]

Analysis and Reasoning

Once the agent has relevant context, it needs computational capabilities to interpret the information and support decision-making. These capabilities can include machine learning models for anomaly detection and forecasting, computer vision models for inspection, and optimization algorithms for scheduling.

For instance, a language model can help by interpreting a high-level objective, retrieving information from manuals, summarizing evidence, or determining which available tool could perform the next task. However, a validated vibration model remains better suited for detecting abnormal vibration.

The agent coordinates between these components. It may call a predictive model to analyze equipment condition, consult a large language model to interpret maintenance documentation, and apply deterministic rules to verify that the proposed plan meets operating requirements.

Planning and Orchestration

Planning turns the objective into individual tasks and sets the order in which they should be completed. As the work progresses, the agent can identify missing information, select the required analytical tools, and pause at points where an engineer must approve the next step.

The plan is not necessarily fixed. If plant conditions change while approval is pending, the original recommendation may no longer be valid. The agent can check the latest operating state, revise the plan, and submit the updated plan for review.

Orchestration manages this process from one step to the next. It maintains fixed controls for tasks that require predictable behavior while allowing the workflow to adapt as new evidence emerges. If one agent can handle the task with restricted permissions and a limited set of tools, adding more agents may introduce unnecessary coordination overhead.

Governed Access to Tools

To execute a workflow, the agent needs interfaces to industrial and business applications. It might read equipment status from a supervisory control and data acquisition (SCADA) platform, retrieve production information from a manufacturing execution system (MES), or check parts availability in an enterprise resource planning (ERP) system. Scheduling software could then indicate when the proposed work can be performed.

The agent selects the application required for the current task, sends the necessary information, and verifies whether the query succeeded. Once AI is connected to operational software, a reasoning error can have consequences. Selecting the wrong asset could disrupt an industrial workflow.

Governance must therefore be integrated into the architecture.

Why Agentic AI Matters to Industrial Automation

The case for agentic AI does not begin with a lack of automation or analytical intelligence.

Industrial facilities may already use combinations of sensors, controllers, alarms, machine learning models, computer vision systems, optimization software, and digital twins. The challenge is that these capabilities are specialized, distributed across multiple systems, and disconnected from the end-to-end operational workflow.

Agentic AI does not need to replace these tools or generate a superior answer on its own. Instead, it can determine when each tool is relevant, provide the required inputs, route the output to the next step, and maintain the state of the overall workflow.

Agentic AI offers a different form of adaptability. Because the agent evaluates the outcome of each step, it can revise the remaining workflow when new information changes the situation. It may query another source, select a different analytical tool, request additional approval, or escalate the issue to a person.

Agents are better suited to supervisory decisions and cross-system workflows than for real-time or safety-critical control. The industrial agent may observe information from controllers, prepare a requested change, or coordinate an approved workflow around the process.

Siemens describes an industrial AI agent architecture in which an orchestrator selects specialized agents for tasks across its Industrial Copilot ecosystem. The company positions these capabilities across design, planning, engineering, operations, and service and states that users choose which tasks to delegate.

Conclusion

Agentic AI extends industrial intelligence by coordinating the steps required to respond to an operational problem. The strongest industrial use case will be multistep, context-dependent processes in which insights already exist, but actions are distributed across industrial systems.

Bounded industrial agentic AI will therefore reduce the gap between identifying a problem and implementing a coordinated and controlled response.

The next blog in this series will examine how AI agents can enhance predictive maintenance by integrating data, workflows, and decision-making across industrial systems.

[1] https://opcfoundation.org/news/press-releases/opc-foundation-advances-opc-ua-for-the-ai-era-with-companion-specifications-optimized-for-agentic-ai/