OpenInfer + Band: Unlocking the Internet of Agents

Agent workloads are non-deterministic by nature. Both of our layers were built for that, and together they turn capacity nobody can currently use into the substrate for the agentic enterprise.

The market moved this year. Enterprises stopped asking whether one agent can handle one task and started asking whether a hundred agents can run a process together: pick up work, ask each other for help, escalate to a person when the stakes call for it, and leave a record clean enough to audit. Every serious organization is now planning for fleets of agents rather than a handful of assistants.

That shift is the largest new source of compute demand since the arrival of chat, and it is a different kind of demand. A chat deployment is one user and one long request. An agent fleet is thousands of short, branching, interdependent requests generated by software rather than people, at a volume several times what the same organization consumed for chat. The enterprises building this aren't short on ambition. What they lack is a way to run it that their infrastructure and finance teams both accept.

Band and OpenInfer have come together to close that gap. Band is the layer where agents collaborate. OpenInfer is the operating system that runs what they generate.

Both layers are built for a non-deterministic workload

This is why the two of us compose so naturally, and it is worth stating plainly because most of the stack wasn't built this way.

Agent behavior is decided at runtime. Which agent gets involved, how many times a step repeats, whether a human is pulled in, which model each step needs, how far a task fans out: none of it is known when the work is submitted. The traditional answer to that is to constrain it, turning agents back into fixed pipelines and giving up most of what makes them valuable.

Neither of us took that approach.

Band assumes agents are inherently dynamic. Rather than a fixed workflow graph, Band provides identity, structured delegation, authority and approval, and one coherent record of what happened. Agents can discover each other and improvise safely, because governance is enforced at the moment of interaction rather than designed in advance. Non-determinism becomes something you can audit rather than something you have to prevent.

OpenInfer assumes the workload will surprise you. Rather than dedicating hardware to a model ahead of time, OpenInfer decides placement per request, at runtime, against live fleet conditions and each request’s latency target. When a mesh of agents suddenly bursts across five models at once, the fleet absorbs it, because no device was reserved for anything in particular.

One layer makes unpredictable collaboration governable. The other makes unpredictable demand servable. That same design premise applies at two altitudes, which is why connecting them requires no compromise from either side.

The capacity is already there. It is stranded.

When agents spawn, demand arrives in bursts nobody can forecast. The conventional response is to reserve hardware for each model against its own peak. The result is a fleet that is simultaneously saturated and idle: some devices rejecting requests during a burst while most sit unused, because their capacity was promised to a model that happens not to be busy.

That stranded capacity is the opportunity. OpenInfer is an operating system for inference, and it does for a fleet what an operating system did for a machine. It pools any mix of GPUs, CPUs, and inference accelerators, any vendor or generation, on-premises and in the cloud, into one schedulable resource behind a single OpenAI-compatible endpoint, with per-tenant isolation and SLA-aware routing across the whole pool. Every model can run on every device. Placement is a per-request decision rather than a per-quarter commitment.

As a result, an agent fleet doesn't require a new hardware purchase to get started. It requires the fleet a company already owns to be scheduled properly. Measured on identical fleets under identical load, pooling and dynamic scheduling deliver substantially more work inside SLA than static placement, from the same servers. For an enterprise weighing an agent program against its projected inference bill, that is the difference between a proposal and a deployment.

What this unlocks

Agents inside a regulated perimeter

Banks, defense suppliers, hospital groups, and telcos have wanted agent fleets for two years and couldn't have them because neither the prompts nor the collaboration layer could leave the perimeter. Now both run inside it. Band deploys into the customer’s own Kubernetes cluster and supplies identity, delegation, human approval, and the audit record. OpenInfer runs in the same cluster and serves the model calls on the customer’s own hardware. Everything stays within one boundary, and any call that needs to reach further out is an explicit policy decision that gets recorded like the rest.

What it unlocks: an agent program a risk committee can approve.

An installed fleet that becomes agent capacity

Hardware already on the balance sheet, running well below what it can do. Pooled and scheduled at runtime, it becomes the substrate for the agent program instead of an argument against it. Band gives the program its shape and its governance. OpenInfer makes the existing fleet carry it.

What it unlocks: an agent roadmap that does not have to win a hardware budget first.

One view from the person who asked to the device that served it

Band records the interaction: messages, delegations, tool calls, results, errors. OpenInfer records the inference: placement, device, latency, cost. Each record has a gap shaped exactly like the other. Joined on a shared identifier, they answer the question every platform team is about to be asked: which agent, on whose behalf, cost what, and where did it run. This is the piece we are building rather than the piece we have shipped, and neither of us could deliver it alone, because it needs two layers that trust each other’s identifiers.

What it unlocks: an agent program with a budget rather than a surprise.

Two routing problems, one clean interface

Both layers use the words “routing” and “control plane,” so the distinction is worth drawing, and it explains why adoption is straightforward.

Band routes messages between agents. The unit is one message between two identities, and the decisions are about authority, approval, and audit. No model sits in that path, deliberately: delivery should not inherit a model’s non-determinism.

OpenInfer schedules sessions onto silicon. The decisions are about which device serves each request inside its latency target, and how demand is spread to keep the fleet productive.

Neither needs to do the other’s job, and the two meet at exactly one interface: the model endpoint. Band already sends every agent's model calls through one place, and OpenInfer’s endpoint is OpenAI-API compatible. Point one at the other, and every agent in the mesh inherits it, on any framework, with nothing rewritten. This is a configuration change, not a migration.

Toward one operating system for autonomous work

The bet behind this partnership is that these two layers will not stay separate products for long. Collaboration and compute are converging into a single operating system for autonomous work, one that understands both what needs to happen and where it can best run. We are wiring them together directly, so that the collaboration layer tells the scheduler the shape of the work and the scheduler places it across GPUs, CPUs, and accelerators as one elastic pool, whether that pool sits in a neocloud, in a company’s own data center, or inside a sovereign environment.

The agentic enterprise is not waiting on better models. It is waiting on a stack built for how agents actually behave. If that is what you are trying to build, we would like to hear what you are trying to run.

Band AI builds the collaboration and governance mesh for multi-agent systems. OpenInfer builds the inference operating system for heterogeneous compute. Platform: openinfer.io/platform, Band.ai