ClawNet: Human-Symbiotic Agent Network for Cross-User Autonomous Cooperation
Summary: arXiv:2604.19211v1 Announce Type: new
Abstract: Current AI agent frameworks have made remarkable progress in automating individual tasks, yet all existing systems serve a single user. Human productivity rests on the social and organizational relationships through which people coordinate, negotiate, and delegate. When agents move beyond performing tasks for one person to representing that person in collaboration with others, the infrastructure for cross-user agent collaboration is entirely absent, let alone the governance mechanisms needed to secure it. We argue that the next frontier for AI agents lies not in stronger individual capability, but in the digitization of human collaborative relationships.
To this end, we propose a human-symbiotic agent paradigm. Each user owns a permanently bound agent system that collaborates on the owner’s behalf, forming a network whose nodes are humans rather than agents. This paradigm rests on three governance primitives:
- Layered Identity Architecture: This architecture separates a Manager Agent from multiple context-specific Identity Agents. The Manager Agent holds global knowledge but is architecturally isolated from external communication.
- Scoped Authorization: This mechanism enforces per-identity access control and escalates boundary violations to the owner, ensuring that users have full oversight of their agent’s actions.
- Action-Level Accountability: This feature logs every operation against its owner’s identity and authorization, ensuring full auditability and transparency in agent interactions.
We instantiate this paradigm in ClawNet, an identity-governed agent collaboration framework that enforces identity binding and authorization verification through a central orchestrator. This setup enables multiple users to collaborate securely through their respective agents, fostering an environment where productivity can flourish across various tasks and interactions.
ClawNet represents a significant advancement in the field of AI by addressing the limitations of current agent frameworks. By facilitating collaboration among agents that represent different users, it opens up new possibilities for enhanced productivity and efficiency in human endeavors. This framework not only empowers users to leverage their agents more effectively but also ensures that their collaborative interactions are governed by robust security and accountability measures.
As organizations increasingly rely on AI to enhance productivity, ClawNet’s human-symbiotic approach may serve as a model for future advancements in agent technology. By digitizing human collaborative relationships, it is poised to redefine how individuals and teams interact with AI systems, ultimately leading to more cohesive and productive workflows.
In conclusion, ClawNet offers a pioneering framework for cross-user autonomous cooperation, marking a new chapter in the evolution of AI agents. As this technology develops, it will be essential to monitor its impact on collaboration, productivity, and the ethical considerations that arise from the integration of AI in human relationships.