Model and agent infrastructure

Some products expose intelligence directly.

Others provide the runtime, control plane, data, and developer experience that make intelligent products reliable.

CPU application infrastructure now External inference now Managed model runtime coming soon GPU workloads require GPU capacity

Infrastructure products

Agent sandbox platform

Provide isolated execution, files, networking, previews, snapshots, and lifecycle APIs to another application.

Browser agent platform

Expose persistent browser identity, action APIs, recordings, and customer-level isolation.

Model API product

Wrap one or more models in a typed API with authentication, quotas, queues, and usage reporting.

Evaluation platform

Execute test suites, compare model or agent behavior, preserve traces, and detect regressions.

Agent observability

Collect runs, tool actions, screenshots, costs, decisions, failures, and replay evidence.

Approval and policy layer

Intercept sensitive actions, evaluate policy, request human authority, and issue durable receipts.

Data processing API

Turn documents, media, websites, or business records into structured output through durable jobs.

Agent application builder

Give another agent a safe workspace where it can create, preview, and publish complete applications.

Model endpoint architecture

flowchart LR
  Client["Customer SDK or application"] --> Gateway["Authentication and API gateway"]
  Gateway --> Queue["Queue and admission control"]
  Queue --> Scheduler["Hardware-aware scheduler"]
  Scheduler --> Runtime["Model runtime"]
  Runtime --> Artifacts["Weights and caches"]
  Runtime --> Result["Stream or result"]
  Result --> Gateway
  Gateway --> Usage["Usage, cost, and evidence"]

A production endpoint needs more than a running model server.

It needs typed inputs, authenticated access, concurrency limits, queue behavior, cancellation, streaming, health checks, artifact provenance, usage records, and rollout controls.

Open model interfaces

OpenAI-compatible model servers such as vLLM make it easier to separate the application from the model runtime.

That lets the application use a consistent API while the underlying model, inference engine, and hardware placement change.

Self-hosted AI applications can connect to those endpoints without embedding model execution into the web process.

Hardware-aware execution

Future managed model execution should make these requirements declarative:

Requirement	Examples
Accelerator	CPU, GPU family, GPU count, memory
Artifact	Container image, model weights, adapters
Serving mode	Online endpoint, batch job, queued generation
Scaling	Minimum replicas, maximum replicas, scale to zero
Data	Input objects, output objects, cache volume
Networking	Private, public, or workspace-only
Proof	Health, model identity, artifact digest, live route

Build the control plane first

You can build the customer experience, API, billing, queue, data model, and external-provider integration now. The model execution boundary can move to MIOSA-managed GPU capacity when that capability is available.

Infrastructure for infrastructure

MIOSA can also serve as a substrate for products that expose their own higher-level API.

Use external attribution to associate every sandbox, computer, deployment, and unit of usage with the downstream customer who created it.

Keep MIOSA credentials on your server and issue narrowly scoped browser access only when the user needs direct preview or terminal interaction.

Build a white-label platform Use MIOSA resources behind your own product, customer model, and billing experience.