ReMAPS: Orchestration Through Continuous Re-Planning in MAS

Multi-Agent Systems (MAS) involve multiple autonomous agents interacting within an environment to achieve individual or shared objectives. Recent progress in artificial intelligence and machine learning-particularly in multi-agent reinforcement learning (MARL)-has markedly expanded MAS capabilities, enabling complex scenarios of cooperation and competition. Still, orchestrating multi-step tasks among multiple agents remains a core challenge, given the need to coordinate roles, manage dynamic environments, and maintain continuous adaptation. Leading AI scholars and research laboratories emphasize that robust multi-agent orchestration is a critical enabler for scaling autonomous systems to real-world complexity. In response to this need, we propose ReMAPS (Recursive Multi-Agent Planning System), a novel framework premised on hierarchical, recursive task decomposition and adaptive re-planning. This paper provides a comprehensive review of current MAS architectures and orchestration methods, situates ReMAPS as a logical and timely breakthrough, and elaborates on the theoretical and technical underpinnings of our approach. We conclude with validation strategies that demonstrate the feasibility, scalability, and significance of ReMAPS for multi-step agent collaboration.