In the paper "From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models," researchers propose a comprehensive implementation roadmap for developing Economic World Models (EWMs). Unlike traditional models, EWMs function as generative engines designed to simulate economic evolution by modeling heterogeneous agents, their internal belief systems, and their interactions within market and institutional frameworks. The authors introduce a hierarchical six-level capability ladder to guide development, progressing from basic rule-based agent environments to sophisticated, LLM-based adaptive worlds, self-evolving agents, and evolving institutional structures. The ultimate goal is the creation of "sim-to-real" economic twins that maintain alignment with real-world empirical observations. Through a systematic literature survey, the study identifies a significant research gap: while current work focuses heavily on low-level agent simulations, there is a scarcity of systems featuring endogenous institutional evolution, persistent empirical alignment, and validated economic mechanisms. The authors position these proposed EWMs as high-fidelity sandboxes for human decision-making and as essential substrates for the training, planning, evaluation, and safety testing of autonomous AI agents. To facilitate this transition toward agentic economies, the researchers have released a curated repository of resources and a paper list to support the academic community in building these complex, self-evolving economic ecosystems.