For decades, industrial companies have pursued the same fundamental goal: building production environments that balance resilience, efficiency, and responsiveness. While this challenge remains just as critical today as it was generations ago, the rules of the game have changed, dramatically.

The traditional approach of relying on steady, incremental improvements, simply cannot keep pace with the unprecedented scale and speed of change now required to operate in an increasingly volatile, disruptive and competitive environment. What's needed, instead, are production systems that don't just gradually get better, but those that are capable of dynamic adaptation and continuous learning.

For this reason, Agentic AI has become increasingly attractive for industrial companies. It naturally fits as the next evolutionary step for accelerating workflow-based improvements to reach the ultimate goal: building production environments capable of agile learning and dynamic adaptation to changing conditions.

However, the path from concept to operational reality remains unclear for most organizations. Welcome to our blog series, The Blueprint for Agentic AI in Industrial Operations, offering a systematic framework for operationalizing autonomous intelligence at scale across industrial enterprises. It addresses three fundamental challenges that prevent industrial companies from capturing the full value of Agentic AI:

First, the infrastructure gap. Traditional batch-based data architectures and rigid point-to-point integrations cannot support the real-time responsiveness that agentic operations demand. Industrial AI agents need continuous streams of contextualized operational data to detect emerging issues, evaluate options, and act before problems escalate or opportunities are lost.

Second, the intelligence gap. Having access to real-time data is insufficient. Agents require semantic understanding; knowledge of how equipment relates to processes, how parameters influence quality outcomes, and what constraints govern safe operations. Without this structured operational intelligence, agents remain reactive data processors rather than autonomous decision-makers.

Third, the trust gap. Deploying autonomous systems that directly influence physical operations introduces risks extending beyond data to equipment integrity, regulatory compliance, and human safety. Organizations need comprehensive governance frameworks that enable confident deployment while maintaining absolute operational control.

In part 1 of this blog series, The Blueprint for Agentic AI in Industrial Operations, we will explore how to establish the foundational data architecture required for agentic operations through a three-step evolution: comprehensive digitization of operations, adoption of event-driven MQTT architecture for real-time data flow, and implementation of Unified Namespace for semantic consistency across the enterprise. Let’s dive in.

Why Agentic AI in Industrial Operations Needs Streaming Data

Fundamentally, agentic AI in industrial operations is about autonomy and agility. That is, detecting anomalies, making operational decisions, and adapting to changing plant conditions as they emerge. This demand for agility arises from the fundamental nature of industrial intelligence itself, which is the ability to respond to production dynamics as they change. Human operators do not process machine conditions in fixed intervals; rather, they react to them as they happen. In the same way, agentic AI systems need to react instantly, drawing on extensive domain knowledge while adapting continuously to live operational conditions.

In agentic systems, freshly streamed data from IoT devices, PLCs, or MES systems brings immediate operational relevance into the agent's context by leveraging recent production state. This real-time data enables timely, autonomous decisions grounded in the most relevant available operational knowledge. Unlike batch processing, which relies on delayed historian queries, streaming data provides a continuous flow of up-to-the-moment machine telemetry, process variables, and quality metrics. This empowers industrial AI agents to act autonomously in real time, rather than waiting for insights that may already be outdated or irrelevant to current production conditions.

While the benefits of streaming data for agentic AI are clear, many industrial organizations still face a critical challenge: how to actually implement this capability across complex, legacy environments. Unlocking the power of real-time data for agentic AI requires a deliberate, phased transformation of your data infrastructure. One that respects the constraints of operational technology while laying the groundwork for scalable, intelligent systems.

This section outlines a three-step roadmap for evolving your data architecture, starting with digitization, enabling real-time flow, and establishing shared semantic context, to progressively unlock the core capabilities required for agentic operations at scale.

Step 1: Digitize Operations to Build a Real-Time Foundation

Before you can stream data, you must first ensure that data exists in digital form. This may seem obvious, but many manufacturing operations still rely on manual data collection, paper-based records, or isolated systems that don't expose their data externally. The top priority is comprehensive digitization; onboarding machine data and sensor telemetry from across your production environment.

This digitization effort begins with an assessment of your current state. Identify which assets, processes, and quality parameters are already instrumented and which remain analog or disconnected. Industrial equipment often comes with built-in connectivity through OT protocols like OPC UA, Modbus, or Ethernet/IP, but older equipment may require retrofitting with sensors and edge devices to capture critical operational data.

The goal is not just connectivity for its own sake, but rather creating a complete digital representation of your physical operations. This includes process variables, equipment states, quality measurements, energy consumption, and contextual metadata that gives meaning to raw sensor readings.

This is where most companies need to fundamentally change their thinking. Rather than only collecting data to enable a specific agentic operation or support a dedicated application, you must instead create an up-to-date digital model of your entire enterprise at all times. One that is not tied to isolated use cases.

Why does this matter for agentic AI? Consider a practical example: when a quality issue emerges on a production line, AI agents need comprehensive operational context to respond effectively. They must rapidly determine not just what went wrong, but how it impacts delivery commitments, which suppliers need to be notified, how production schedules should be adjusted, and which customers require proactive communication. This level of intelligent coordination is only possible when agents have access to a complete, real-time digital representation of operations across manufacturing, supply chain, quality, and planning systems.

However, to turn your digitized operations into data intelligence at scale, your architecture must support four essential capabilities. Let’s explore what they are and why they matter.

Industrial Data Architecture Capabilities Essential for Scaling Agentic Operations

Seamless OT/IT Integration

Your data infrastructure must enable you to easily and quickly add new digital tools and bring legacy systems into your digital ecosystem, without requiring weeks of custom engineering every time something changes or when you onboard a new machine. When every integration demands heavy customization, scaling or deploying agentic AI becomes slow and unsustainable.

Unified Data Accessibility

Your infrastructure must do more than simply collect information from OT and IT systems. It must standardize data, add critical context, and unify it across your organization. This means making clean, real-time data available through a common access layer so that teams of AI agents across operations and maintenance can access and act on a consistent, shared view of operational reality.

Scalable Data Architecture

Agentic AI thrives when it can observe, reason, and act across dynamic, distributed environments. That’s why the ability to seamlessly scale from one production line to many, and from a single facility to an entire global network, is essential. This level of scalability requires a data architecture that enables you to plug in new assets, lines, or sites without rearchitecting your entire system.

High Data Availability for Always-On Operations

In modern manufacturing, downtime isn't just a productivity issue; it's a data issue. If your data pipeline breaks, your visibility disappears, and your agentic AI systems become blind to operational reality. High availability means your data infrastructure must be resilient, fault-tolerant, and designed for continuity.

Step 2: Adopt Event-Driven Architecture for Real-Time Adaptability

Once your operations are digitized and streaming-ready, the next step is to fundamentally shift how data flows across your enterprise. Most manufacturing organizations still rely on traditional point-to-point integrations, where systems connect directly to one another in rigid, custom-engineered pipelines. This architecture is inherently hard to scale and poorly suited to the dynamic responsiveness agentic AI demands.

To unlock the real power of AI agents operating in real time, you need to transition to an event-driven architecture. Specifically, a publish-subscribe model that enables decoupled, continuous, and intelligent data movement across your operations.

At the center of this architecture is the MQTT broker, which serves as the central nervous system of your data infrastructure. Instead of applications and systems being tightly coupled through direct connections, they now interact through a common broker:

This architectural decoupling delivers immediate benefits across all four essential capabilities highlighted earlier:

Seamless OT/IT Integration: No need for complex, bespoke integrations. Systems publish or subscribe to your data infrastructure.

Unified Data Accessibility: All data flows through a shared infrastructure, available in real time to any authorized AI agent.

Scalability by Design: Adding a new machine, application, or AI agent requires no architectural changes.

High Availability: Modern MQTT brokers support clustering and failover, ensuring continuous data flow and system resilience.

Why Events Matter: The Time Value of Information

The defining characteristic of this architecture is that it is event-driven. Data is transmitted as discrete events that reflect what’s happening in your industrial operations right now. These could be sensor updates, machine state changes, operator actions, or quality measurements.

This event-centric view of data is critical for agentic AI systems, which depend on the time value of information. The sooner an AI agent can observe a change and act on it, the greater the impact of that action.

Events may be short-term in nature, such as a production line breakdown, equipment anomaly, or quality deviation. They may be medium- to long-term, such as changes in customer product requirements and the associated modifications to the product design itself, to the industrial process, and to related processes in purchasing, quality, and service.

Each of these events carries a potential business impact, and the window for effective response is often narrow. Traditional architectures, relying on polling, historian queries, or batch integration, introduce delays that blunt the effectiveness of agentic AI-driven responses.

In contrast, event-driven architecture ensures that AI agents see events as they happen, enabling real-time reasoning, rapid decision-making, and high-impact intervention while the event is still relevant.

Operationalizing this model starts with deploying MQTT-enabled gateways and applications throughout your operations. These gateways serve as protocol translators, converting OT protocols into MQTT messages.

Instead of maintaining hundreds of point-to-point integrations, each with polling overhead and custom logic, you now have a many-to-many communication fabric where systems simply publish what they know and subscribe to what they need.

For example, if an AI agent monitoring energy optimization wants to start analyzing compressed air usage, it simply subscribes to that topic. No new connectors, and no integration project.

Ultimately, adopting an event-driven architecture transforms your organization into a digitally responsive enterprise, where AI agents are fully aware of what's happening, when it happens, and are empowered to act before problems escalate or opportunities are lost.

To achieve this, your infrastructure must ensure persistent real-time streaming from edge to cloud, universal connectivity across legacy and modern systems, and open access to operational data for all authorized agents and applications. With this in place, you give your AI agents fresh, actionable data they need to deliver intelligent, adaptive decisions at scale.

Step 3: Establish a Unified Namespace for Semantic Consistency

Digitization creates data availability. Event-driven architecture enables data flow. But for agentic AI to truly thrive, you need semantic consistency. A common understanding of what data means across your entire enterprise. This is where the Unified Namespace (UNS) approach becomes transformative.

A UNS creates a semantic hierarchy that organizes all operational events in your MQTT infrastructure using a standardized structure aligned with your business context. Rather than each system maintaining its own naming conventions and data models, the UNS provides a single, enterprise-wide layer where data is published with consistent semantics. This hierarchy typically follows the ISA-95 equipment hierarchy model, but can be extended with domain-specific context relevant to your operations.

Consider the pharmaceutical manufacturing example: instead of cryptic tags like "PLC_007_AI_023," data is published to meaningful topics like "Enterprise/PharmaSite/Building2/TabletLine1/BlendingUnit/BlendUniformityPV." Every stakeholder, from operators to engineers to AI agents, understands exactly what this data represents and where it originates in the physical plant.

Simply put, AI agents subscribe to hierarchical topics in the UNS and receive data in predictable, well-understood formats, and they can dynamically discover available data streams, understand their context, and incorporate them into decision-making without manual integration efforts.

Perhaps most critically, the Unified Namespace serves as the foundation for interoperability across your technology ecosystem. Whether you're deploying AI agents, feeding data to historians, supporting MES applications, or enabling analytics platforms, they all consume from the same semantic layer. This eliminates the data silos that traditionally force organizations to choose between competing systems, and instead enables an open architecture where best-of-breed applications work together seamlessly.

Establishing Real-Time Data Flow for Agentic AI Through Streaming and Unified Namespace

Example Use Case of Data Streaming for Agentic AI in Industrial Operations

To see how these three foundational steps work together in practice, consider a pharmaceutical tablet manufacturing line facing a common challenge: maintaining consistent quality while optimizing throughput and minimizing waste.

During a production run, tablet weight begins trending toward specification limits while energy consumption unexpectedly increases. In traditional operations, this scenario might go undetected until the next quality check, potentially resulting in out-of-spec product, production delays, and regulatory documentation burdens.

With the infrastructure we've outlined in place, the response unfolds differently:

Real-Time Awareness Through Streaming Data

Because operations have been fully digitized, sensors across the line continuously stream critical parameters, blend uniformity, compression force, tablet weight, coating thickness, temperature, humidity, and energy consumption, creating a complete digital representation of production conditions as they evolve.

Event-Driven Intelligence

Through the event-driven MQTT architecture, a monitoring agent immediately detects the trending anomaly as it emerges. Rather than waiting for scheduled historian queries or batch updates, the agent observes the deviation in real time, capturing the precise operational context: which product is running, what batch phase is active, and how current conditions compare to normal operating ranges.

Semantic Understanding Through the UNS

When the AI agent subscribes to the topic Enterprise/PharmaSite/Building2/Line1/BlendingUnit/BlendUniformityPV, it receives not only the current value but also the full context: specification limits, batch identity, recipe parameters, and relationships to upstream blending and downstream coating processes.

Collaborative Intelligence at Scale

Armed with this semantic understanding, the monitoring agent shares the anomaly context across a network of specialized agents through the same UNS infrastructure. The quality agent retrieves relevant SOPs and previous batch records. The process optimization agent analyzes the correlation between feeder speed, compression dwell time, and tablet weight variability. The predictive maintenance agent identifies developing vibration signatures from the coating pan bearing that correlate with the energy consumption spike.

Continuous Improvement

As production continues, the agents monitor outcomes, comparing actual results against their predictions. This feedback, yield improvement, reduced variability, energy consumption correction, flows back through the same infrastructure, continuously refining agent decision models and expanding the organization's operational intelligence.

Together, these foundational capabilities transform agentic AI from a theoretical concept into operational reality, enabling autonomous, intelligent responses that preserve quality, optimize performance, and adapt continuously to changing production conditions.

Conclusion

Real-time operational awareness is the cornerstone of agentic AI. With digitization, event-driven architecture, and semantic consistency in place, your enterprise is finally ready for the next step: transforming streaming data into contextual intelligence. In the next blog, Enabling Contextual Intelligence for Agentic AI in Industrial Operations, we dive into semantic graphs and ontologies that are critical components for enabling industrial agents to reason, not just react. Stay tuned.