Beyond Decoupling: How CESMII’s i3X Solves the Context Engineering Gap for Industrial Data Fabrics
Originally Published March 2026, on ARCweb.com by Colin Masson, Director of Research for Industrial AI, at ARC Advisory Group Inc.
In my last post, we explored the "Data Decoupling Debate" and confronted a harsh reality: tearing down data silos and piping everything into a Unified Namespace (UNS) or a data lake is only half the battle. Moving data is not the same as understanding it. If we want to scale Agentic AI, we must bridge the semantic gap.
When Anthropic open-sourced the Model Context Protocol (MCP) late last year, it sent shockwaves through the industrial software ecosystem. Anthropic initiated a new wave of modernization by providing a standardized “universal translator” that allows AI agents to securely connect to enterprise data sources. In effect, it solved a major connectivity and security challenge for enterprise AI.
However, MCP is essentially a grammar framework. It still needs a vocabulary. If an AI agent uses MCP to ask a factory’s data broker for “pump telemetry,” the system still needs to know exactly what a “pump” looks like in the data structure.
This brings us to an emerging development in the industrial interoperability space: CESMII’s Industrial Information Interoperability Exchange (i3X). The increasing engagement across the vendor ecosystem suggests that i3XTM could become an important semantic framework supporting Industrial AI initiatives.
The Federal Mandate: 50 Percent Cost Reduction and the End of the Silo
If you have worked in manufacturing IT for any length of time, the pattern is familiar. Organizations purchase tools, connect machines, and build dashboards, only to find themselves dealing with significant technical debt months later. Much of this stems from “API chaos.” Every vendor exposes a different API, and every system integrator maps tags differently.
Recently, the conversation has begun to shift. CESMII CEO John Dyck has highlighted stagnant manufacturing productivity and pointed out that CESMII’s federal mandate is to reduce the cost and time required to implement smart manufacturing by 50 percent. Achieving this type of improvement requires standardized interfaces rather than continued reliance on custom integration code.
As CESMII Chief Technology Architect Jonathan Wise has explained, i3X promotes Smart Manufacturing Profiles, reinforcing the idea that “data needs contracts.” Instead of a situation where one system publishes temperature in Fahrenheit, another in Celsius, and another as a filtered string, i3X enforces consistent data definitions.
Importantly, CESMII is not attempting to replace existing standards or impose legacy approaches on modern developers. While i3X works with foundational OT structures such as OPC UA NodeSets, the standard also provides these data models in JSON Schema format.
This approach aligns closely with common IT development practices. JSON Schema is widely used and understood by modern enterprise developers and AI systems. It helps bridge the gap between OT rigidity and IT agility by replacing one-off integrations with reusable, structured data objects.
This shift also introduces an important architectural change. i3X allows developers to build products or AI agents against a standardized API rather than proprietary stacks. As Dyck noted: “If you're a manufacturer, you should absolutely never buy another data silo, never buy another stovepipe architecture.”
The Ecosystem Rallies: Fixing the “API for the UNS”
One reason i3X is attracting attention is that it is not limited to academic discussions. The vendor ecosystem is actively engaging with the initiative, and developers are already downloading the i3X Explorer from GitHub to experiment with the specification.
As Aron Semle, CTO at HighByte and a contributor to the i3X working group, explained, i3X can be viewed as the “API for the factory” or the “API for the UNS.” However, as Jonathan Wise clarified in a recent discussion, i3X goes beyond supporting the UNS concept. It also addresses one of its major limitations.
The preferred UNS technology, MQTT, is highly effective for centralizing live, event-based data streams, but it does not inherently enforce semantic consistency. i3X addresses this by requiring access to historical data, relationships, and a structured type system, complementing the UNS with additional semantic capabilities.
This potential value also attracted the attention of my colleague Craig Resnick, ARC’s Vice President and Lead Analyst for Industrial Automation, who joined me during a recent briefing with the CESMII leadership team. Craig works closely with automation suppliers such as Rockwell Automation, Schneider Electric, and Siemens. His perspective was that i3X could allow automation vendors to reduce the effort spent building custom data extraction layers and instead focus on higher-level analytics and AI-driven control and optimization applications.
A growing coalition of technology providers is now participating in the working group supporting the specification:
A Critical Evolution: From State to Action (Methods and Services)
While the momentum is undeniable, a significant question has emerged regarding the ultimate goal of Agentic AI: closing the loop.
Agentic AI is not limited to interpreting data. It may also initiate actions. If an AI agent reads the state of a pump through i3X and determines that the asset is failing, how would it execute a Reset() or Calibrate() function?
Currently, the i3X specification supports “writes,” which could theoretically be used to represent method calls. However, this approach is less structured than the function execution models found in platforms such as OPC UA or ThingWorx.
When I raised this issue with members of the i3X working group, the response was encouraging. Aron Semle confirmed that the discussion around “writes versus methods” is an active topic within the group. The working group is evaluating the addition of standardized methods where actions accept a defined argument model and return a defined response model, either in the initial release or in a subsequent update.
Because i3X is built on a modern REST-based architecture, extending the specification to include methods is technically feasible. The greater challenge will involve implementing these actions within heterogeneous industrial environments, where legacy systems and edge platforms must coordinate execution.
The fact that the working group is already considering this capability suggests that it recognizes the broader objective: Agentic AI requires both a standardized vocabulary for state and a standardized vocabulary for action.
Recommended by LinkedIn
Stress-Testing the Vision: Answers from the Architect
Beyond the methods gap, several broader architectural and commercial questions remain. I asked Jonathan Wise to address some of the most significant market and implementation issues. His responses helped clarify several areas of confusion in the industry.
1. The Agentic AI Connection: How quickly can i3X actually power AI?
Wise: "Applying MCP to manufacturing data is dramatically simplified by i3X. An MCP server is already on the list of official deliverables for the i3X Working Group, and the community has already started building them."
2. Alignment with OPA and ODTA: Are we creating competing standards?
Wise: "i3X is agnostic to, but preserves, the underlying type system—as long as there is one. OPAF and ODTA implementations can be bound to i3X, and the API will protect the semantics and models from those systems, requiring consumers to use the model as designed instead of re-interpreting the semantics."
3. The Brownfield Reality Check: How does i3X handle decades of legacy PLC spaghetti code?
Wise: "i3X is a common API for brownfield-wrapping platforms. i3X is not itself a platform; it sits on top of a platform that does that heavy lift—and ensures we never have the problem again."
4. The Proprietary Data Fabric Friction: Does an open API threaten the valuation of major data fabric platforms (like Cognite) by commoditizing their context engines?
Wise: "i3X commoditizes the API layer, not the value of the underlying platform. It makes interfacing easier, but it doesn’t limit the massive value that Cognite (and others) can provide."
5. Intellectual Property vs. Open Semantics: If an OEM publishes a profile of their equipment to the i3X marketplace, how do they protect their proprietary physics and trade secrets?
Wise: "This confuses three separate concepts. The information model (the profile) is abstract—it contains no data. The data and the algorithm that computes results can be kept entirely private. The definition of the API is open, but the implementer of the API decides, through modern authentication and authorization, exactly who gets to consume the models, results, and data."
The Verdict: Scaling the Autonomous Enterprise
The industrial sector is entering a new phase of AI adoption. Protocols for AI reasoning and connectivity, including MCP and emerging agent frameworks, are evolving rapidly.
However, intelligence requires context. Without a standardized semantic layer, many AI deployments risk remaining highly customized implementations that are difficult to scale.
CESMII’s i3X initiative represents one of the most significant industry-backed efforts to standardize that semantic layer. If widely adopted, it could help standardize industrial data models and simplify the development of Industrial AI applications across complex operational environments.
Bring on Hannover Messe.
Are you evaluating how to integrate Agentic AI into your operations? I’d love to hear how your team is handling the "API Chaos" and semantic mapping challenges. Reach out to me or the team at ARC Advisory Group to discuss how the emerging i3X standard might impact your Industrial Data Fabric roadmap.
Engage with ARC Advisory Group
The Industrial AI (R)Evolution is moving faster than ever. To dive deeper into the frameworks and data shaping the future of the industrial sector, explore my latest research:
Where do YOU stand in the Industrial AI (R)Evolution? Take our Industrial AI Assessment to benchmark your organization's maturity, identify critical gaps in your IT/OT/ET convergence, and get actionable recommendations to accelerate your path to becoming an Industrial AI Pacesetter.
Don't guess what your global operations or prospective customers need. Use empirical data to align your stakeholders and de-hype the market with ARC Advisory Group's Voice of Market Service.
For tailored recommendations on governing and guiding major people, process, and technology decisions across the enterprise, cloud, industrial edge, and AI, please contact Colin Masson at cmasson@arcweb.com.
Or, set up a meeting with my fellow Analysts and I at ARC Advisory Group to find out more about our Executive Insights Service for Industrial organizations and our Industrial AI Insights Service for Vendors.
Colin Masson as telemetry volumes explode, the next frontier is real‑time predictive insights across infrastructure stacks. That’s exactly what we’re solving at Event Sentinel AI correlating signals across network, storage and compute to alert teams before degradation becomes an incident.We are Looking for Enterprise teams to pilot our predictive platform — reach us at gabriele@eventsentinel.ai #AI #AIOps #PredictiveAnalytics #Observability #Telemetry #RealTimeData #MachineLearning #ITOperations #EnterpriseIT #CloudComputing
You've hit the nail on the head. Even with emerging standards like MCP and i3X, the fundamental problem remains: someone still has to do the brutal, heavy lift of mapping decades of legacy spaghetti code to the new standard. A standard API is a great target, but it doesn't solve the problem of translating the thousands of non-standard, proprietary formats that already exist in the wild. That's the part that kills projects. We are focused on being the "platform that does that heavy lift." Our AI is designed to learn any data format, from legacy EDI to proprietary factory floor data, and translate it into a structured, usable model automatically. We provide the semantic understanding on the fly, without requiring a massive, manual mapping project to a new standard. It seems like our approach is the missing piece of the puzzle you've laid out. Let's connect.