AI AGENTS COLLABORATION UNDER RESOURCE CONSTRAINTS: PRACTICAL IMPLEMENTATIONS

Abstract

AI agents that operate in teams (multi-agent systems) can solve complex problems beyond the capability of any single agent.However, collaboration in such systems is often limited by resource constraints, including finite computation power, energy, and communication bandwidth.This research examines how AI agents coordinate effectively under these constraints, surveying key frameworks and algorithms and highlighting practical implementations.We discuss strategies for resource-aware coordination, from negotiation protocols to multi-agent reinforcement learning, and provide case studies in robotics, IoT networks, and emergency response.Our evaluation synthesizes empirical results, demonstrating that collaborative agents can achieve significant performance gains (e.g., faster task completion and resource savings) despite limited resources.We also outline future research directions to enhance multiagent collaboration in constrained environments.The findings have broad implications for designing efficient, resilient AI agent teams in real-world applications.