Autonomous AI Agents and Multi-Agent Systems for Next-Generation Intelligent Computing

Abstract

The rapid advancement of artificial intelligence is driving a transition from conventional AI systems that primarily generate predictions or respond to individual commands toward autonomous AI agents capable of planning, reasoning, tool use, learning and executing multi-step tasks. Autonomous AI agents represent an emerging paradigm in intelligent computing in which software entities can perceive their environment, formulate goals, make decisions and perform actions with limited continuous human intervention. When multiple autonomous agents interact, cooperate or compete within a shared environment, they form Multi-Agent Systems (MAS). This paper examines the foundations, architectures, capabilities and emerging applications of autonomous AI agents and multi-agent systems for next-generation intelligent computing. It discusses agent perception, reasoning, planning, memory, communication, tool use, learning and action execution, together with centralized, decentralized and hybrid multi-agent architectures. The paper further investigates applications in robotics, intelligent transportation, healthcare, cybersecurity, software engineering, smart cities, finance and scientific research. Particular attention is given to agent coordination, communication protocols, task allocation, emergent behaviour, trust, security, explainability and human oversight. The paper argues that the combination of large language models, reinforcement learning, distributed computing and agentic architectures is creating new possibilities for autonomous problem-solving. However, reliability, hallucination, goal misalignment, security vulnerabilities, excessive autonomy and evaluation difficulties remain major challenges. The paper concludes that future intelligent computing systems will increasingly combine human intelligence with networks of specialized AI agents operating collaboratively across digital and physical environments. Achieving this vision will require robust architectures, reliable memory and planning mechanisms, secure inter-agent communication, standardized evaluation frameworks and effective human governance.