arXiv:2606.14445v1 Announce Type: cross Abstract: Existing multi-agent software development systems have proposed many forms of agent collaboration, including role-based collaboration and automated code review. However, many systems assume a common runtime, a central conversation server, or the same API family. Under these assumptions, LLM agents from different vendors cannot easily exchange messages directly from their own execution environments while dividing development and review work on a shared codebase. This paper presents tap, a file-based collaboration protocol that allows Claude (Ant
The proliferation of various LLM agents from different vendors necessitates a common, flexible protocol for effective collaboration that bypasses vendor-specific lock-ins and centralized servers.
This development allows disparate AI agents to work together seamlessly on complex tasks, crucial for scaling autonomous systems across diverse enterprise environments.
The ability for heterogeneous LLM agents to easily exchange messages and share a codebase via a file-based protocol fundamentally changes how multi-agent systems can be designed and deployed, increasing interoperability.
- · LLM agent developers
- · Enterprises adopting multi-agent systems
- · Open-source AI foundations
- · Proprietary multi-agent platforms with closed ecosystems
Heterogeneous LLM agents can now participate in more complex, distributed software development workflows.
This improved interoperability accelerates the development and deployment of truly autonomous AI agents across various industries.
The abstraction layer provided by file-based protocols could foster a 'web of agents' where diverse AIs collaborate on a global scale.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI