Agentic AI automation of UNICOS application specification processes

This project is part of the CERN OpenLab summer student programme, hosted by the BE-ICS (Beams Department, Industrial Control Systems) group. It falls under the broader Industrial Edge-Cloud and AI-based Agents initiative, a collaboration between CERN and Siemens exploring how AI can integrate with industrial control systems.

We are building an AI-powered copilot agent to automate time-consuming and error-prone tasks in the UNICOS (UNified Industrial Control System) application development process. UNICOS is CERN’s framework for developing PLC-based control applications used in cryogenics, cooling, HVAC, and other continuous processes across the accelerator complex.

Project Goal

The core objective is to build an AI-powered copilot agent that assists expert users in writing comprehensive and accurate Functional Analysis documents and specification files. The agent performs two key functions:

1. Functional Analysis assistance: helps users draft project specifications by checking for completeness and consistency, proactively requesting missing details, and ensuring a well-defined project scope.
2. Specification transformation: automatically converts user-written textual Functional Analysis into the structured spreadsheet format required by UNICOS for automated PLC application generation.

This bridges the gap between high-level requirements and the structured specification needed for development.

Approach

We are building an MCP (Model Context Protocol) server that integrates directly with the UNICOS development workflow. The server exposes tools to an AI agent capable of:

• Analyzing user-provided text to identify missing information and inconsistencies.
• Extracting key requirements and translating them into structured data.
• Generating suggestions and prompts to guide users toward complete specifications.

The MCP server architecture keeps the AI logic separate from the UNICOS tooling, making the system modular and framework-agnostic.

Roles

Daniya is leading the implementation:

• Designing and implementing the MCP server in Python: architecture, tool definitions, and API integration.
• Working with CERN experts to understand the Functional Analysis contents and the UNICOS specification file format.
• Developing the conversion logic from natural language requirements to structured spreadsheet cells.
• Testing and evaluating accuracy, reliability, and usability of the assistant.

I’m supervising the project, providing guidance on the MCP architecture, AI agent design, and integration with the existing UNICOS ecosystem.

This project gives BE-ICS a hands-on experiment with AI-driven automation in industrial control systems, with the potential to significantly reduce specification errors and accelerate project delivery.