Synergy: A Next-Generation General-Purpose Agent for Open Agentic Web
Xiaohang Nie, Zihan Guo, Kezhuo Yang, Zhichong Zheng, Bochen Ge, Shuai Pan, Zeyi Chen, Youling Xiang, Yu Zhang, Weiwen Liu, Yuanjian Zhou, Weinan Zhang

TL;DR
Synergy introduces a new agent architecture designed for persistent, collaborative, and evolving AI agents that operate within the open, decentralized Open Agentic Web ecosystem, emphasizing social integration and lifelong learning.
Contribution
The paper presents Synergy, a novel general-purpose agent framework enabling persistent, social, and adaptive behavior for agents in open web environments, addressing limitations of isolated or closed agents.
Findings
Synergy enables agents to participate in open collaboration networks.
Agents demonstrate lifelong learning and adaptation over time.
The architecture supports social identity and continuous evolution.
Abstract
AI agents are rapidly expanding in both capability and population: they now write code, operate computers across platforms, manage cloud infrastructure, and make purchasing decisions, while open-source frameworks such as OpenClaw are putting personal agents in the hands of millions and embodied agents are spreading across smartphones, vehicles, and robots. As the internet prepares to host billions of such entities, it is shifting toward what we call Open Agentic Web, a decentralized digital ecosystem in which agents from different users, organizations, and runtimes can discover one another, negotiate task boundaries, and delegate work across open technical and social surfaces at scale. Yet most of today's agents remain isolated tools or closed-ecosystem orchestrators rather than socially integrated participants in open networks. We argue that the next generation of agents must become…
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