Agent Communication Protocols
As multi-agent systems try to move past the limits of standalone agents, communication becomes the load-bearing infrastructure, and the protocol landscape for it is a fragmented mess. This study builds a technical taxonomy to classify and compare LLM agent communication protocols and to make the interoperability problem legible.
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A five-dimensional taxonomy: Following an established iterative method, the authors classify protocols along counterparty, payload, interaction state, discovery mechanism, and schema flexibility, derived through five iterations over nine actively maintained open-source protocols with real adoption.
Recurring architectural patterns: Every sampled agent-to-agent protocol combines hybrid payloads with session-state persistence, most support multiple predefined schemas, and two negotiate schemas at runtime, signaling a clear trend toward schema flexibility.
Where the gaps are: Decentralized discovery remains rare, and the analysis suggests short-term convergence pressure toward protocols that unify agent-to-agent and agent-to-context communication for tools and data.
Why it matters: No single protocol is likely to maximize versatility, efficiency, and portability at once, so the field will probably evolve into a federated, layered protocol stack, and this taxonomy gives teams a way to choose protocols and surfaces open problems like privacy and policy enforcement.