AI agents are transforming the industrial world. But to work together effectively, they need a shared understanding of the context in which they operate. The Model Context Protocol (MCP) creates precisely this foundation—as an open standard for the exchange of meaning, not just data. oee.ai integrates MCP and demonstrates how agents can use it to intelligently connect planning, production, and maintenance.
Artificial intelligence has reached the factory—not in the form of individual models, but as networked agents that independently understand tasks, exchange contexts, and prepare decisions. For these agents to work together effectively, they need a common language. This is provided by the Model Context Protocol (MCP)—a new, open standard that currently forms the basis of many future-oriented AI infrastructures.
Why MCP is important for the industrial future
Until now, production systems, planning software, and AI models have each spoken their own language. The Model Context Protocol fundamentally changes this: It enables agents from different systems—whether MES, ERP, or maintenance platforms—to share their context. This allows agents to understand not only data points, but also their meaning: What is a shift? What does “machine stopped” mean? What does “planned order postponed” mean? Only with this shared semantic context can agents act autonomously, precisely, and safely.
oee.ai integrates MCP – and paves the way for industrial agents
With the integration of MCP, oee.ai is part of this new agent landscape. Our platform can not only analyze data but also provide contextualized information via MCP that other agents can use – and vice versa. This makes oee.ai an active participant in an agent network that links production, planning, and maintenance. MCP ensures that every agent “knows” what is at stake and can automatically prepare the appropriate decisions.
Three application levels of the MCP:
- Within oee.ai, agents coordinate internally: e.g., a data quality agent, an OEE optimization agent, a reporting agent.
- Within a company – agents from different systems (MES, ERP, maintenance) act on the basis of common contexts.
- Across company boundaries – agents securely exchange information with suppliers and partners.
In this article, we focus on Level 2 – internal enterprise use, where agents enable end-to-end automation across system boundaries for the first time.
How MCP agent technology works
Agents are specialized software units that can understand, execute, and learn from tasks. For example, a production agent can monitor machine status, a planning agent can reprioritize appointments, or a maintenance agent can trigger actions. The MCP ensures that all agents communicate via the same context space. So, if oee.ai detects that a line is down, the planning agent automatically understands that orders need to be postponed – without human intervention and without the systems being directly integrated with each other.
The result is an adaptive, self-organizing system that responds to events before they cause problems.
Example 1: Proactive capacity and order replanning
In a traditional scenario, a production planner must respond to unplanned downtime manually. With MCP and AI agents, things are different: A production agent from oee.ai reports an impending delay to a production order. A planning agent receives this context via MCP, checks current production orders, capacities, and priorities—and automatically proposes an optimized replanning. The decision can be reviewed by a human or implemented directly.
Agent: “Machine A has been running too slowly for 45 minutes. I’ve moved order #123 to line B to meet the Friday delivery date. The change has been scheduled in the ERP, and the team leader has been informed.”
This transforms static planning into a dynamic, learning system.
Result: Shorter response time, higher adherence to deadlines, better utilization of resources.
Systems: oee.ai + ERP (specifically production planning) + communication platform (e.g. Teams)
Example 2: Closed-Loop Maintenance Agent
MCP also opens up new possibilities in maintenance. An oee.ai agent detects subtle patterns—such as increasing micro-stops or rising scrap rates. It transmits this context to a maintenance agent, which decides whether an action is necessary, orders parts, or adjusts the planning. After the action is completed, the data flows back, and the agents independently improve their models.
Agent: “Vibration on spindle #4 has increased by 30%. Last service: May 2025. Spare bearings < 2 pcs – I have created job W-882 and prepared purchase order #PO-773.”
This creates a closed learning cycle that extends from maintenance and planning to production – a true closed loop.
Result: Fewer unplanned downtimes, more precise maintenance, continuously learning systems and a measurable contribution to overall equipment effectiveness.
Systems: oee.ai + CMMS + ERP (specifically: inventory situation, procurement process)
Advantages of agent communication via MCP:
- Automated collaboration: Agents act independently across system boundaries.
- Faster decisions: Reaction times are reduced because systems share context in real time and 24/7.
- Greater transparency: Every agent “understands” the current state of the factory.
- Scalable intelligence: Knowledge is shared, not duplicated – the company learns as a whole.
Conclusion: From data silo to agent economy
The Model Context Protocol ushers in a new phase of industrial digitalization: Data alone is no longer the deciding factor, but rather the ability of agents to act together. By integrating MCP, oee.ai becomes a hub of this agent economy—where context, AI, and industrial experience converge. Companies that embrace this architecture early on are laying the foundation for adaptive, autonomous factories.
Learn how MCP and AI agents can connect your factory. Learn more at www.oee.ai or email info@oee.ai.