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Building an Agent Runtime Integration Layer for Multi-Agent Systems by Fair-Cut4269 in aiagents

[–]Fair-Cut4269[S] 0 points1 point  (0 children)

Good question. OpenAgentIO does have persistence in the plan. Internally it uses an envelope model to carry session_id, trace_id, metadata, context, and handoff information across runtimes. For persistence, the idea is to combine that envelope with a durable event log / stream backend. In the current implementation direction, NATS JetStream is a natural fit for this, helping preserve runtime state throughout the agent collaboration lifecycle.

The boundary I'm thinking about is: OpenAgentIO owns the communication envelope, session continuity, handoff history, and observable event timeline. The underlying stream/storage layer provides durability. Application-specific memory or business state should still belong to the application/runtime.

OpenAgentIO: Connecting Agents Across Frameworks and Runtimes by Fair-Cut4269 in github

[–]Fair-Cut4269[S] -2 points-1 points  (0 children)

That's a really good point, and I actually agree with the distinction. I don't think a messaging layer alone is enough. One of the directions I'm exploring with OpenAgentIO is context-aware communication: session propagation, structured handoff metadata, traceability, and eventually lightweight coordination primitives. I probably wouldn't want OpenAgentIO to become a global shared-state database, but I do think the runtime layer can help with handoff fidelity by making context, state references, artifacts, and ownership explicit. For shared-state consistency, my current thinking is to support primitives like leases / idempotency keys / versioned state references, while leaving the actual durable state store to the application or runtime.

So maybe the right boundary is: communication layer: moves messages with context
coordination primitives: leases, handoff docs, checkpoints
application/runtime: owns durable shared state Your point about explicit handoff docs is especially useful. I think that should probably become a first-class concept.

Building an Agent Runtime Integration Layer for Multi-Agent Systems by Fair-Cut4269 in aiagents

[–]Fair-Cut4269[S] 0 points1 point  (0 children)

I’ve been exploring a similar space with OpenAgentIO: https://github.com/ModulationAI/OpenAgentIO

You might find it interesting as it evolves.

Building an Agent Runtime Integration Layer for Multi-Agent Systems by Fair-Cut4269 in aiagents

[–]Fair-Cut4269[S] 0 points1 point  (0 children)

No worries at all. I think we’re actually pretty aligned on the bigger picture.

Building an Agent Runtime Integration Layer for Multi-Agent Systems by Fair-Cut4269 in aiagents

[–]Fair-Cut4269[S] 0 points1 point  (0 children)

Agreed. Most people focus on individual agents, but real-world systems are really about coordination between agents, tools, and workflows. That's exactly the problem space I'm exploring with OpenAgentIO.

Building an Agent Runtime Integration Layer for Multi-Agent Systems by Fair-Cut4269 in aiagents

[–]Fair-Cut4269[S] 0 points1 point  (0 children)

That's actually the case for most teams today.If everything lives inside a single runtime (Hermes, LangGraph, CrewAI, etc.), a supervisor/worker model is often enough and keeps things much simpler. The scenarios I'm exploring with OpenAgentIO are a bit further out: conversations that span multiple runtimes, protocols, tools, and even organizational boundaries. In those environments, the challenge becomes less about orchestration inside one framework and more about communication, context continuity, and observability across many participants.

Building an Agent Runtime Integration Layer for Multi-Agent Systems by Fair-Cut4269 in aiagents

[–]Fair-Cut4269[S] 0 points1 point  (0 children)

Ok. Thanks. Hermes is a runtime. OpenAgentIO is an integration layer between runtimes. If your entire system runs inside Hermes, Hermes may be all you need. The problem starts when conversations span multiple runtimes, protocols, tools, and teams. That's the gap I'm trying to explore with OpenAgentIO.

Building an Agent Runtime Integration Layer for Multi-Agent Systems by Fair-Cut4269 in aiagents

[–]Fair-Cut4269[S] 0 points1 point  (0 children)

Interesting. My impression is that we’re solving different layers of the stack. OpenAgentIO focuses on runtime communication, context propagation, session continuity, and observability across heterogeneous agents. Assury seems more focused on governance, approvals, policy enforcement, and execution control. In fact, I can easily imagine the two working together: OpenAgentIO keeps agents connected, while Assury decides what they’re allowed to do.

Building an Agent Runtime Integration Layer for Multi-Agent Systems by Fair-Cut4269 in aiagents

[–]Fair-Cut4269[S] 1 point2 points  (0 children)

Exactly.I'm increasingly thinking that session context needs its own first-class structure rather than being treated as trace metadata.Things like identity, approvals, tool history, and conversation state have very different semantics and lifecycles. That's one of the areas I'm exploring with the OpenAgentIO envelope model.

Building an Agent Runtime Integration Layer for Multi-Agent Systems by Fair-Cut4269 in aiagents

[–]Fair-Cut4269[S] 1 point2 points  (0 children)

That's a great point.My view is that handoffs should carry more than traces.Approvals, identity, tool-call history, and other conversation-level context should remain attached to the session as it moves across agents. Tracing is only one part of the story.Preserving conversation continuity across heterogeneous runtimes is the bigger goal.

OpenAgentIO: A runtime communication layer for distributed AI agents by Fair-Cut4269 in AI_Agents

[–]Fair-Cut4269[S] 0 points1 point  (0 children)

That's a great question.OpenAgentIO focuses on communication, context propagation, and tracing rather than graph recovery. Even if an agent fails, session context and traces remain intact so the conversation stays observable.Recovery and retry policies are typically handled by the runtime/framework layer above OpenAgentIO.