Manufacturing Needs More Than Dashboards

Most factories have plenty of data flowing from machines and systems, but that data rarely drives timely action. Dashboards and alerts help operators spot problems, and SQL queries let analysts dig into issues—but when something needs fixing, a human still has to notice, decide, and intervene.

This delay creates a bottleneck. To break it, we need systems that don’t just surface problems but understand goals and take action. That’s where agents come in. They monitor, decide, and respond — often autonomously or sometimes in collaboration with humans — all in context. It’s not automation in the old sense; it’s autonomy with purpose, shaped by safety, compliance, and human judgment.

Why First Resonance Built an MCP Server

Manufacturing environments consist of loosely coupled systems such as PLCs, SCADA, MES, ERP, and various custom services, each with its own schema, protocol, and interface. There is no shared contract or query layer across these systems, which makes integrating agents brittle and limits contextual understanding.

We built the Model Context Protocol (MCP) server to provide agents with a consistent, typed interface to these systems. Today, it connects to all ION databases, exposing live and historical data as structured context models. In the future, it will integrate machine protocols, APIs, and human inputs.

The goal is to separate agent logic from low-level system complexity while maintaining the flexibility to reason and act across multiple domains.

What You Can Do with the First Resonance MCP Server

The MCP server is not a traditional data connector or message broker. It is a structured context layer — a shared manufacturing environment model that enables agents to operate semi-autonomously, reliably, and at scale across diverse systems. Here are the core capabilities it provides:

Contextual Querying of Live and Historical Data

Agents can request live sensor readings, machine states, and process variables with precise contextual filters — by time window, equipment hierarchy, or production batch. The MCP server integrates with ION databases to provide consistent access to historical trends alongside real-time telemetry. This lets agents build accurate situational awareness models and detect deviations or patterns that inform decision-making.

Orchestrating Multi-Agent Workflows

Manufacturing challenges often require collaboration across specialized agents — scheduling, quality control, logistics, and maintenance. The MCP server supports agent coordination by managing shared context, event subscriptions, and workflow handoffs. This decentralized orchestration enables scalable, flexible solutions that can evolve organically as factories change.

Dynamic Registration of New Agent Capabilities

The MCP server’s extensible design allows new agents and capabilities to register themselves at runtime. This means customers or integrators can introduce custom logic or domain-specific reasoning without modifying core systems. The MCP handles these agents' discovery, authentication, and lifecycle management, enabling a plug-and-play ecosystem.

Standardized APIs for Defining and Managing Agent Goals

The MCP server exposes APIs designed to let third parties define agent objectives, constraints, and evaluation metrics in a consistent format. This formalization opens the door for domain experts to begin tailoring agent behavior without deep programming knowledge..

In short, the MCP server is the enabling infrastructure that transforms isolated data points and control endpoints into an integrated, context-aware environment for agents. It’s not just a messaging system — it’s the foundational layer that turns passive factories into proactive, adaptive systems.

Agents: Beyond Dashboards, Beyond Chatbots

Dashboards let operators monitor key metrics but require manual intervention to act. Chatbots respond to direct queries without initiating actions or handling complex workflows. SQL queries give raw data access but don’t translate that data into meaningful decisions.

We build ION Agents to fill this gap. They maintain internal state over time, track evolving conditions, and run continuous decision loops to adjust plans dynamically. Agents can initiate control commands autonomously based on real-time data and collaborate with other agents to coordinate tasks across different systems.

This combination of memory, autonomy, and collaboration enables agents to move manufacturing systems from passive monitoring to proactive control.

These aren’t just hypothetical: They’re real agent deployments illustrating how agentic systems go beyond dashboards and alerts to drive meaningful outcomes.

Enabling Customers to Bring Their Own Agents

The MCP server is designed with openness in mind so that customers can bring their own agent implementations. Why does this matter? Because no two factories are alike. Each customer brings unique domain knowledge, toolchains, and proprietary processes. By supporting external agents as first-class participants in the system, the MCP enables a plug-and-play model that lets factories extend and evolve their capabilities without changing core infrastructure.

Real Agent Use Cases We’ve Implemented

Agents powered by the MCP server handle complex, real-world manufacturing tasks beyond data queries or alerts. Here are several  concrete examples from our deployments:

1. Automated Ordering

An agent detects low inventory of engine controllers and prepares an order with Acme Corp, verifying specifications and delivery schedules. A buyer reviews and confirms the proposed order, reducing manual effort and accelerating procurement.

2. Drawing-to-Procedure Conversion

Given a 2D engineering drawing, an agent identifies the corresponding manufacturing procedure step (e.g., procedure-32-step-10) and links it contextually in the workflow system, streamlining documentation and execution.

3. Root Cause Suggestions

When an issue arises, an agent pulls from historical data and knowledge bases to suggest common fixes, enabling faster troubleshooting without waiting for expert intervention.

4. Supplier Part Tracking

An agent scans vehicle build data to identify which vehicles have parts from a flagged supplier (e.g., “Bad Supplier”) installed. Then, the agent generates a report listing those vehicles along with their locations.

5. Workcenter Availability

Before a production run, an agent queries real-time workcenter status to find available capacity for a specific job, dynamically adjusting schedules to optimize throughput.

6. Automated FAA Failure Reporting

For compliance, an agent generates a PDF failure report for FAA issues by aggregating sensor data, maintenance logs, and inspection results, ensuring accuracy and reducing manual report generation time.

What Agent Workflows Look Like

Here’s what a typical agent workflow looks like:

1. For example, the agent receives a goal: “Reduce downtime by 10%”.
2. It queries the MCP server to gather relevant data and understand the current system state.
3. Based on this, it builds a contextual model and proposes actions.
4. It executes those actions autonomously or in collaboration with a human operator.
5. It monitors the outcome and adapts future decisions accordingly.

This closed-loop workflow allows agents to operate with autonomy and accountability, continuously learning from outcomes to refine their decisions over time. Agents become reliable partners in driving operational improvements by staying grounded in real-time context and aligned with high-level goals. Whether optimizing quality, reducing waste, or responding to unexpected events, they act purposefully—amplifying human intent rather than replacing it.

Architecture Benefits: Composability and Resilience

Decentralized agent logic reduces reliance on monolithic systems, allowing agents to operate autonomously, which improves flexibility and simplifies maintenance.

Enables streamlined quality engineering trials across multiple production lines or facilities, allowing rapid validation of process improvements or parameter changes without impacting overall manufacturing stability.

Naturally fits microservices and event-driven architectures, enhancing scalability and responsiveness in complex manufacturing environments.

Improves fault tolerance by ensuring that the failure of one agent doesn’t compromise the entire system, maintaining smooth operations even during component issues.

Why This Changes the Game

• Moves from manual monitoring to proactive problem-solving, with agents autonomously detecting and addressing issues in real time instead of operators reacting to dashboards.
  
  
• Replaces static rule maintenance with adaptive learning, allowing agents to evolve their behavior dynamically based on data and changing conditions.
  
  
• Empowers domain experts over IT bottlenecks, enabling experts to deploy and manage agents directly in their language and context, speeding up innovation and factory responsiveness.
  
  

Agentic manufacturing points to a future where the factory thinks, adapts, and evolves, with human and machine working symbiotically. And piece by piece, that future is already unfolding through early deployments and open, flexible infrastructure.

Conclusion: From Automation to Autonomy

The agentic factory isn’t a distant vision; it’s already taking shape, piece by piece. What’s making it possible is open, flexible infrastructure like the MCP server paired with intuitive agent interfaces. The next leap forward won’t come from any one company but instead from collaboration among engineers, manufacturers, researchers, and system builders. If you’re working on this future, we want to hear from you.

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