In RP1, the Consortium focused on contextual understanding by advancing from state-of-the-art analysis (WP2) to KIO-level operationalisation (WP3). Partners applied a comprehensive assessment of existing GenAI solutions, identifying key gaps in mainstream tools regarding traceability, compliance, privacy, and interoperability. The requirements for engineering process were strengthened with the UI-REF reference framework, where requirements were prioritised according to this methodology, and this prioritisation was explicitly assessed by 6 software engineers to ensure practical relevance. To support this, a dynamic working environment utilising GitHub Projects and a Kanban-style strategy was established for requirement engineering, versioning, and open-source integration. The team specified the platform architecture (KIO1) as a microservices layer where core modules operate as autonomous AI agents, designed to orchestrate the Agentic workflow components into unified DevOps/CodeOps modules aligned with UC-driven knowledge graphs. A core achievement in this period was the operationalisation of an ontology-driven requirements engineering methodology. The consortium successfully designed and matured UC-specific ontologies (notably for UC2 and UC4), which serve as the semantic foundation for transforming natural language needs into machine-interpretable knowledge graphs. Significant progress was made in technical building blocks, including AI-driven requirements formalisation and evaluation (KIO3, KIO4), which enable the transformation of natural language needs into machine-interpretable ontologies linked to architectural blueprints. Furthermore, AI-based code analysis and generation (KIO2, KIO7) were substantiated to provide closed-loop mechanisms where execution traces are fed back for iterative improvement. Initial developments in synthetic data generation (KIO5) and automated data preparation (KIO6) were operationalised, supported by a scalable data warehouse concept to ensure provenance-aware data handling. Concurrently, the team specified the AI-Powered Test Automation Tool (KIO11) and initiated the Energy-Efficient Model-Driven TinyML module (KIO10) to support intelligent cyber-physical systems, all underpinned by a compliance-by-design framework embedding security and privacy KPIs aligned with the EU AI Act.
