Advancements in large language models(LLMs)have markedly improved the adaptability of artificial intelligence(AI)agents in dynamic and open environments.However,with the growing number and diversity of agents,ensuring...Advancements in large language models(LLMs)have markedly improved the adaptability of artificial intelligence(AI)agents in dynamic and open environments.However,with the growing number and diversity of agents,ensuring secure,reliable,and autonomous collaboration among them has become an urgent and critical challenge.To this end,this letter proposes agent reinforced generation(ARG)to establish a multi-agent system with audit trail functionality,privacy compliance,and autonomous coordination.ARG integrates the model context protocol(MCP)and agent-to-agent(A2A)protocol to define the rules and logic governing agent-to-agent communications as well as agent-to-tool/data engagements.Decentralized autonomous organizations and operations(DAOs)are employed to enable agents to coordinate and execute tasks in a transparent and tamper-resistant manner.Additionally,the operational process of ARG is elaborated from task issuance to completion to validate the auditability and immutability of task coordination and execution.Finally,we highlight five key features of ARG,including parallelism and throughput,scalability across domains and load,fault tolerance and graceful failure,resource efficiency through delegation,as well as data security and privacy protection,positioning it as a promising paradigm for the realization of agentic intelligence.展开更多
基金supported by the Science and Technology Development Fund,Macao SAR(Nos.0093/2023/RIA2 and 0145/2023/RIA3).
文摘Advancements in large language models(LLMs)have markedly improved the adaptability of artificial intelligence(AI)agents in dynamic and open environments.However,with the growing number and diversity of agents,ensuring secure,reliable,and autonomous collaboration among them has become an urgent and critical challenge.To this end,this letter proposes agent reinforced generation(ARG)to establish a multi-agent system with audit trail functionality,privacy compliance,and autonomous coordination.ARG integrates the model context protocol(MCP)and agent-to-agent(A2A)protocol to define the rules and logic governing agent-to-agent communications as well as agent-to-tool/data engagements.Decentralized autonomous organizations and operations(DAOs)are employed to enable agents to coordinate and execute tasks in a transparent and tamper-resistant manner.Additionally,the operational process of ARG is elaborated from task issuance to completion to validate the auditability and immutability of task coordination and execution.Finally,we highlight five key features of ARG,including parallelism and throughput,scalability across domains and load,fault tolerance and graceful failure,resource efficiency through delegation,as well as data security and privacy protection,positioning it as a promising paradigm for the realization of agentic intelligence.
