But then I hit a wall. I realized I hadn't built a bridge; I'd built a walled garden.
The mesh was great at internal coordination, but it had zero ability to introduce itself to an external entity. There was no standard way to ask a foreign swarm what its capabilities were, no way to hand off a task that another framework could understand, and no common protocol for task cancellation or progress streaming. I had a society with no border crossings.
The difference between coordination and interoperability #
It’s easy to confuse these two, but in a real-world AI workflow, they are worlds apart.
SMESH handles coordination. It’s modeled after mycorrhizal networks rather than traditional job queues. In my implementation:
- Agents emit signals into a local field.
- Signals lose intensity over time (preventing stale data bloat).
- Agents claim work based on local affinity.
- Consensus is reached when independent agents reinforce the same claim.
- There is no central scheduler; if a claim isn't supported, it just vanishes.
This is perfect for answering "Which specialist should handle this?" or "Is this signal just noise?" within a closed system.
However, interoperability is what happens when an outside agent needs to interact with SMESH. This is where the Agent2Agent (A2A) protocol comes in. A2A isn't a "brain" for the agent; it's a public contract. It allows agents from different vendors and frameworks to discover each other and collaborate without leaking private memory, internal tools, or proprietary planning logic.
Implementing the A2A layer #
To make SMESH useful in a multi-agent ecosystem, I had to map my internal logic to the A2A specification. The protocol provides a standardized vocabulary that acts as a gateway. Instead of my agents just "emitting signals," they now respond to formal A2A requests.
If you are looking into building an LLM agent that needs to work in a heterogeneous environment, these are the core concepts you need to implement: AgentCard: This is your handshake. It advertises identity, specific skills, available interfaces, and security declarations.Message: A typed interaction turn.Task: This is crucial for long-running operations. It tracks stateful work through a lifecycle so an external caller can actually monitor progress.Artifact: The structured result data returned once a task hits a terminal state.contextId: The glue that groups related messages and tasks together into a single session.
The A2A v1 model is quite flexible regarding the transport layer, allowing for bindings via JSON-RPC, gRPC, or even standard HTTP/REST. For my Rust implementation, I'm looking closely at how to map A2A operations like
SendMessage
, GetTask
, and SubscribeToTask
to my existing QUIC-based mesh.The goal isn't to replace the decentralized, signal-based logic of the mesh, but to wrap it in a layer that an external agent can actually negotiate with. Without that boundary, even the most sophisticated agent swarm is just an isolated island.
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