Colour, Material and Finish (CMF) designers face rising circularity demands but lack tools that combine reliable data, traceable reasoning and creative control. This paper reports a case study with automotive CMF designers, identifying pain points in data access, evaluation of circular options, authorship and trust in AI. We propose design requirements and a conceptual model for agentic AI systems that support circular CMF work while preserving designer agency, accountability, and confidence in material decisions.

With the introduction of SysML v2—featuring a new metamodel and standardized APIs—new opportunities arise for automating MBSE tasks through generative AI. However, existing LLM approaches lack interoperability due to limited support for complex SysML v2 API operations. This paper presents a framework for implementing Model Context Protocol (MCP) servers for SysML v2, enabling LLMs to interact with system models via the API. This framework provides a foundation for agentic MBSE by allowing automation of modeling tasks while maintaining interoperability across tools and generative AI systems.

There is no doubt that we are now in the midst of an AI-driven revolution in how organisations and their employees work with information. The power of recent GenAI and other deep learning technologies to absorb and process massive amounts of data as well as generate new information in response to natural language prompts has obvious implications for knowledge work. The current developments in more autonomous agentic AI systems alongside the commodification of large language models (LLMs) and reduced barriers to entry for application developers will drive a second wave of innovation over the coming five years. This will cause disruption for many organisations and the workers within them, but such changes seem inevitable. Preparing now to work with these technologies and the opportunities they present as well as mitigate the problems they bring is essential. The opportunities for many information professionals are significant as effectively managing data assets holds the key to competitive advantage in this rapidly changing environment. Here Dr Martin De Saulles, a technology analyst and writer (see page 123 for a review on his new book The AI and Data Revolution: Understanding the New Data Landscape), goes through some of the key points relating to the evolution of AI in relation to those who work with information.