Enabling Contextual Intelligence for Agentic AI in Industrial Operations

Agentic AI in industrial operations can only make safe, reliable decisions when it understands not just real-time data, but the meaning behind that data. While streaming architectures and the Unified Namespace (UNS) establish the foundation for real-time visibility, they do not provide the semantic intelligence required for truly autonomous industrial systems. 

Welcome back to our 5-part blog series, The Blueprint for Agentic AI in Industrial Operations, offering a systematic framework for operationalizing autonomous intelligence at scale across industrial enterprises. In part 1 of the series, we outlined how digitization, event-driven architecture, and the Unified Namespace represent a crucial transformation in how industrial data flows through your enterprise. With these capabilities in place, AI agents and humans can now access real-time operational data through a semantically organized hierarchy. Data streams continuously from edge to enterprise, delivered with consistent naming conventions and hierarchical context. 

However, this semantic hierarchy, while essential, is not sufficient to fully operationalize autonomous, agentic AI. The gap stems from the fact that having data is not the same as having intelligence. To reason, coordinate, and act safely across lines, sites, systems, and geographies, agents need linked meaning, not just well-named streams. 

The Limitations of Semantic Hierarchy in Industrial Data Intelligence

The Unified Namespace solves the problem of data accessibility and basic semantic organization. It tells you where data originates in your physical plant and provides consistent naming. But it doesn't answer the deeper questions that agentic operations demand:

• Why does this equipment failure affect downstream processes in specific ways?

• How do process parameters relate to quality outcomes across different product formulations?

• What are the regulatory implications of adjusting this process variable?

• Which safety protocols must be validated before an agent can autonomously modify setpoints?

These questions demand semantic richness, a deep, structured understanding of relationships, constraints, rules, and context that govern industrial operations. They require transforming data into actionable intelligence through an ontology-driven semantic graph that captures decades of engineering knowledge, regulatory requirements, and operational expertise.

To bridge this gap, industrial organizations must add a semantic graph layer that captures not just where data lives in your plant hierarchy, but what it means in the context of your operations, how it relates to other operational knowledge, and why it matters for decision-making. 

Agentic AI Operations Use Cases Enabled by Graph Relationships

Unlike traditional hierarchical models, semantic graphs capture complex, non-linear relationships that reflect the true intelligence of manufacturing processes. These include how materials flow through a system, which components or processes depend on others, which parameters influence specific outcomes, and the reasons why certain operations may halt or fail. By modeling these relationships, your semantic layer makes your AI agents context-aware, enabling them to reason not just about what is happening, but why it’s happening.

To understand the power of this approach, let’s explore specific industrial use cases that are unlocked when semantic graphs are built using machine-readable relationships.

Intelligent Material Flow Management

The relationship, FeedsInto / ReceivesFrom, shows the physical flow sequence independent of hierarchy:

Without this relationship, you'd only know TabletPress_01 is under PharmaSite/Building2/Production/Compression, not that it comes after the blending unit and feeds the coating pan. This enables AI agents to:

• Calculate lead times through the manufacturing train

• Identify bottlenecks when one unit slows production

• Predict cascade effects of upstream delays

SuppliesTo / FeedsFrom - Direct supply relationships:

When API storage drops below minimum level, monitoring agents instantly know the blending unit will run out of material in approximately 15 minutes, triggering alerts to material handlers before production interruption occurs. When the tablet press reports blend feed interruption, root cause agents immediately query FeedsFrom to identify whether the issue is API storage depletion, excipient supply blockage, or blending unit malfunction.

Dynamic Production Capability Management

The relationship, UsesFormulation, links production line to product formulation:

The line doesn't contain the formulation, but it's validated to produce it. The same formulation can be executed on multiple qualified lines across different sites. Planning agents query: "Which lines can manufacture this product?" 

Autonomous Genealogy & Recall Response

Consumes / Produces - Material genealogy for regulatory compliance and recalls:

If an API supplier issues a recall notice for Lot ACE_4427, traceability agents execute automated genealogy queries:

1. Backward trace: "Which production batches consumed this API lot?" → Identifies Batch_20241113_001

2. Forward trace: "Which finished goods lots contain these batches?" → Identifies FG_20241113_A

3. Distribution query: "What is the distribution status of affected lots?" → Queries ERP for customer shipments

4. Regulatory documentation: Automatically generates recall impact assessment with complete material flow documentation per 21 CFR Part 11

Full forward and backward traceability in seconds rather than hours of manual batch record investigation. Critical for both recall response and routine lot release genealogy verification.

Intelligent Workforce & Asset Coordination

QualifiedFor - Human resource management and qualification tracking:

Scheduling agents query: "Which operators can run Tablet Line 1 on night shift for Aspirin production?" Returns only personnel with both equipment and formulation qualifications. When planning agents schedule a new product introduction, they automatically identify which operators require additional training and estimate training timeline before production can commence.

This prevents scheduling conflicts where operators are assigned to equipment they're not qualified to operate.

Below is a visualization of relationships semantic graphs enable you to capture.

Why Ontologies Matter for Agentic AI in Industrial Operations

For semantic graphs to be truly effective in industrial settings, they must be grounded in domain-specific ontologies. An ontology is a formal, structured representation of knowledge within a particular domain. It defines what things exist (entities), how they relate to each other (relationships), and what each element means in context. Think of it as a blueprint for shared understanding: every term, data point, and relationship has a clearly defined role and meaning.

In industrial domains, standards like ISA-95 (for manufacturing systems) and the Common Information Model (CIM) (for energy systems) serve as foundational ontologies. These standards define a consistent vocabulary and relationship structure that enables interoperability and shared understanding. For example, when a data point is labeled "material lot," ISA-95 ensures that its meaning is unambiguous, and both humans and AI agents trained on the model understand what it represents.

By adopting ontologies like these, agentic AI systems gain the semantic grounding they need to operate reliably across complex environments. Ontologies make data machine-interpretable, enabling AI agents to reason about relationships, dependencies, and intent, rather than merely processing raw data.

A typical industrial ontology includes:

• Classes – Abstract concepts such as Equipment, Process, Material, Product, or Personnel

• Properties – Attributes and relationships like hasTemperature, feedsInto, requiresParameter, or producesOutcome

• Instances – Concrete examples of classes, such as TabletPress_001, AspirinFormulation_A, Batch_20241113, or Jon_Smith

• Constraints – Business rules, valid ranges, or required relationships that enforce data integrity and operational logic

This structured representation allows agentic AI systems to infer, reason, and act based on meaningful relationships within the data. For example, if the ontology defines that a TabletPress must recordCompressionForce for each Batch, an AI agent can proactively identify when this parameter is missing, outside acceptable ranges, or inconsistent with the expected formulation.

Ontologies are essential for enabling this kind of semantic reasoning. They provide the clarity and structure that AI agents need to interpret data correctly, not just based on labels or schemas, but based on what the data actually means in a domain-specific context.

In this way, proper semantic structuring does more than enhance current data utility, it also future-proofs your data ecosystem. As AI agents become more capable and autonomous, their ability to operate effectively will depend on access to well-defined domain semantics.

Building the Semantic Layer for Agentic AI Operations

The strategic value of ontology-driven semantic layers is clear: they transform raw data flow into actionable intelligence, enable AI agents to reason with industrial knowledge, and coordinate autonomous operations across complex manufacturing environments. However, understanding the "why" and "what" is only the beginning. The critical question remains: How do you actually implement this capability in your organization?

The answer lies in selecting or building a data platform with at least four of the essential capabilities below, that work together to create, maintain, and operationalize your semantic foundation. 

Data Modelling: Making Data Agentic AI-Ready

Most plant data isn't inherently AI-ready. Even when labeled and mapped through the Unified Namespace, raw sensor telemetry cannot be directly related across plants, systems, or production contexts. The first step in building semantic intelligence is transforming raw operational data into standardized, AI-ready structures through consistent data models.

Data models must incorporate rich contextual metadata that goes far beyond simple measurements to capture what data means operationally. Critically, these models must automatically adapt as new fields, tags, or process changes are introduced, keeping your foundation always current without requiring manual re-engineering.

By converting all OT data into a small number of scalable, standardized schemas (typically a few core models covering equipment, processes, materials, and quality), manufacturers establish a consistent foundation for improvement across every line and plant. Because every site uses the same core models, agentic AI insights proven in one location can be applied enterprise-wide.

Semantic Graphing: Capturing Relationships and Intelligence

Semantic graphing capabilities enable you to map the relationships between your data models; turning the isolated data structures into a connected network of operational knowledge.  This allows you to bring your ontology to life.

In short, data models specify the structure and context of individual entities, while the semantic graph defines how those entities relate, interact, and influence each other. 

Data Catalog: Enabling Discovery and Transparency

As your semantic layer grows to encompass hundreds of data sources, thousands of entities, and complex relationship networks, discoverability becomes critical. AI agents need mechanisms to discover what data exists, what it means, and how to access it. Human data scientists, engineers, and operators need the same capabilities.

The data catalog serves as the navigational layer for your semantic foundation. It provides a searchable, browsable interface to your ontology, data models, and available data products. 

Moreover, every data asset in the catalog includes rich metadata:

• Business context: What this data represents in operational terms

• Technical details: Format, update frequency, retention period

• Quality indicators: Completeness, accuracy, timeliness scores

• Lineage: Where data originates and how it flows 

• Access controls: Who can use this data and under what conditions

• Usage patterns: Which agents or applications consume this data

Data Governance: Ensuring Trust, Quality, and Compliance

Agentic operations create a new challenge: autonomous systems making operational decisions based on data-driven intelligence. This autonomy demands trustworthy data, data that is accurate, complete, timely, secure, and compliant with regulatory requirements. Without rigorous data governance, AI agents can amplify data quality issues into operational problems.

Data governance for agentic operations must address three critical dimensions:

1. Quality Rules and Validation

Poor data quality results in incorrect aggregated insights and inaccurate feedback, ultimately undermining confidence in both the AI agent systems and their actions. Governance frameworks, embedded directly into the semantic layer, must define:

• Completeness rules: Which data fields are required for specific decisions

• Accuracy thresholds: Acceptable error rates for measurements and calculations

• Timeliness requirements: How fresh data must be for different use cases

• Consistency checks: Cross-validation across related data sources

2. Lineage and Traceability

In many industrial sectors, data lineage, the complete record of data origin, transformations, and usage, is not optional. For agentic operations, lineage serves two additional purposes:

Debugging and Root Cause Analysis: When an AI agent makes an unexpected recommendation, lineage enables you to trace back through the chain of data sources, transformations, and reasoning steps that led to that recommendation. This transparency is essential for building trust in autonomous systems.

Impact Analysis: When a data source changes, a sensor is recalibrated, a calculation formula is updated, an integration is modified, lineage enables you to identify which downstream agents, data products, and operational decisions are affected.

3. Security and Access Control

Agentic operations create new security considerations. AI agents access data across multiple systems, reason about sensitive operational information, and can initiate actions that affect production. Security governance must ensure Fine-Grained Access Control, Audit Logging, Data Masking and Privacy,  and Secure Distributed Access. 

Conclusion

By adding meaning, relationships, and constraints to streaming data, you give AI agents the cognitive foundation they need to operate safely and intelligently. With both real-time data flow and semantic intelligence established, the next step is deciding where agentic AI delivers the highest value. Stay tuned for our next blog in the series, Identifying Agentic AI Use Cases for Operational Efficiency in Industry, where we explore a systematic approach for identifying high-value agentic use cases across four strategic domains, production continuity, throughput optimization, quality assurance, and resource efficiency, with a three-stage maturity framework guiding organizations from diagnostic intelligence through prescriptive recommendations to fully autonomous operations.