Multi-Agent Intelligent System for Dynamic Predictive Evaluation of National and Regional Labour Markets in Bulgaria

Reliable public-sector labour-market forecasting requires models that can be updated as data sources, AI tools, and labour-market signals evolve. This paper proposes a provider-independent multi-agent framework for dynamic predictive evaluation of national and regional labour markets in Bulgaria. Implemented as a Model Context Protocol (MCP) server, the system coordinates specialised agents for data ingestion, preprocessing, semantic extraction, AI-adjusted transformation modelling, automated model evaluation, and reporting through stable input–output contracts. The empirical application integrates Bulgarian Employment Agency administrative registered-unemployment indicators, Eurostat labour-market data, World Bank macroeconomic data, and textual, audio, and video evidence on AI, skills, and employment change. The analysis covers the period 2015–2030. Observed official data are used up to 2025 for model construction and validation, while the 2026–2030 values are reported only as forecast and scenario projections. For youth unemployment among persons aged 24 years or younger, the semantic-enhanced model achieves the best predictive accuracy (RMSE = 0.2033; MAE = 0.1457), representing a small improvement over the structured baseline (RMSE = 0.2057; MAE = 0.1462) and a substantial RMSE reduction relative to the persistence benchmark (RMSE = 0.4750; MAE = 0.2891). The AI-adjusted coefficient does not reduce holdout error relative to the semantic-enhanced model, but provides an explicit and sensitivity-tested mechanism for regional scenario interpretation. Regional forecasts indicate persistent spatial inequality, with the Northwest remaining the highest-risk region and the Southwest the lowest-risk region.