Architecting Distributed General-Purpose AI Agent Networks

The rapid advancement of large language models has shifted AI capabilities from passive conversational assistants to autonomous agents capable of understanding goals, planning actions, using tools, and executing multi-step tasks. However, the effectiveness of a single agent is inherently limited by its local data, tool access, runtime environment, and governance boundaries. This research explores the concept of distributed general-purpose agent networks, envisioning open peer-to-peer systems where diverse agents, deployed on personal devices, edge nodes, or autonomous computing environments, can discover each other, establish trust, negotiate cooperation rules, and collaboratively execute open-ended tasks. The authors argue that simply combining existing peer-to-peer overlays with conventional multi-agent systems is insufficient for creating such networks. Agent networks require the propagation of semantic declarations concerning intentions, capabilities, states, and cooperation constraints. To facilitate this, a layered architecture is proposed, centered on a protocol adaptation layer that bridges high-level task semantics with low-level network operations. Within this architecture, three core mechanism problems are identified: semantic announcement propagation for discovering collaborators, verifiable identity and multi-topic reputation for governing cooperation, and semantic-gradient mechanism design for open task execution. For each problem, the paper outlines technical solutions, including bodyless gossip with sequential logs for propagation, BAID-based identity binding with MG-EigenTrust reputation for trust, and a Stackelberg-style mechanism-generation loop driven by semantic attribution feedback for task execution. Prototype overhead results for BAID-style verification and simulations of MG-EigenTrust under collusion attacks are also presented, laying a foundational framework for open, trustworthy, and scalable agent collaboration.