AI Agent Orchestration

Agent protocols as first-class citizens

Multi-agent systems fail in ways that look nothing like a single model timing out: two agents both claim the same task, a state update arrives out of order and an agent acts on stale information, or a long-running tool call over Server-Sent Events silently drops mid-stream. Bolting agent coordination onto general-purpose messaging middleware — built for arbitrary application events, not for the specific lifecycle of an agent task — is where most of that fragility comes from.

Relay implements the emerging agent standards natively rather than as an SDK wrapper: the Model Context Protocol with its 15 methods and SSE streaming, Google's A2A with AgentCard discovery and task lifecycle, the Agent Communication Protocol's descriptors and run lifecycle, and Relay's own binary Agent Mesh protocol for sub-millisecond coordination between agents that don't need the overhead of a JSON-RPC envelope.

What guaranteed ordering buys you

• →Deterministic task assignment: when two agents could claim the same task, ordering guarantees decide who won without a race condition.
• →Real-time state sync: distributed agents share consistent state without each one polling a shared database.
• →Reliable inference streaming: LLM output streamed over MCP with SSE arrives in order, even under load.

Where it runs

Agent Mesh coordination can run entirely inside your own infrastructure — useful for agent workflows that operate on internal data and can't route through a third-party hosted broker — with the same protocol support whether the cluster is in the cloud or on your own hardware.