Toward a reusable architecture for intelligent agents

Active inference provides a powerful framework for understanding how agents perceive, learn, and act by maintaining an internal model of the world. However, most existing implementations rely on generative models that are designed for specific tasks or domains. These task specific models limit the ability of agents to generalise, adapt to new environments, or scale beyond narrow applications.Taking inspiration from the human brain, which appears to solve the problem of generalisation through a combination of modularity, hierarchy, and embodiment, we propose a list of core ingredients. We outline seven core components that we believe are essential for a scalable and reusable generative model, including compositional structure, temporal abstraction, attention and precision, meta-level learning, separation of self and world, and mechanisms for learning model structure itself.The discretisation of these core components hints at the necessity for a series of interconnected generative models organised on a graph-like structure with (some) shared variables, states and parameters, similar to the functional specialisation, integration, and connectivity of the brain. We propose that these components offer a blueprint for constructing intelligent systems that can generalise across tasks and domains.