In both military and commercial domains, tasks are increasingly entrusted to autonomous systems and robots. These artificial intelligence (AI) systems are expected to be safe and intelligent, adapt to changing environments, and interact with other actors, both automated and human. In this paper, we present a framework and corresponding analytics for developing AI agents that possess (1) cognitive skills, including the ability to perform counter-factual reasoning and self-assessment and to exhibit human-like curiosity, biases, and errors; (2) the ability to learn complex tasks quickly with limited feedback; (3) the ability to coordinate and co-learn with human or AI teammates; and (4) the ability to function well over long time horizons (e.g. hours or days). Our framework is based on the theory of adaptive behavior called active inference, which was developed in computational neuroscience and psychology. Together with learnable deep factorized representations, the active inference provides the objective function, high-capacity predictions, and scalable computational mechanisms that enable AI agents to execute four processes fundamental to human cognition: learning, perception, planning, and simulation. We demonstrate the advantages of our AI solution in the domain of planning multi-agent maneuvers for executing area control missions. Our model achieves faster learning compared to the reinforcement learning baseline, producing faster point accumulation and game win rate.