What you can build with MIOSA

MIOSA is a runtime platform for AI products. It gives your application persistent cloud workspaces where agents can write files, run commands, preview web apps, create artifacts, use browser desktops, and publish durable deployments.

The core product idea is simple:

User request
  -> agent plans work
  -> sandbox does code, files, builds, tests, artifacts
  -> computer does browser, desktop, screenshots, manual-style tasks
  -> MIOSA streams events and previews
  -> deployment ships the finished app

MIOSA is not just a remote shell. It is the execution layer underneath products that need AI agents to do real work safely.

Common product categories

Build Lovable, Bolt, v0, or Replit-style products where an agent creates a web app inside a sandbox, opens a live preview, fixes errors, and publishes.

Let end-users generate intake forms, landing pages, dashboards, automations, and workflow apps inside your branded platform.

Generate proposals, PDFs, DOCX files, slides, HTML reports, CSV exports, charts, SVGs, ZIPs, and downloadable client deliverables.

Use computers for browser sessions, login flows, screenshots, form testing, visual QA, and human handoff.

Run Python or Node jobs that clean data, call APIs, generate charts, produce reports, and store results.

Host agent loops that use MIOSA sandboxes, computers, MCP tools, secrets, files, previews, and deployments.

Build Nebula-style virtual-device products where agents share persistent devices, files, browsers, jobs, and app connections.

Build agents that research, browse, code, generate artifacts, use connected apps, and execute multi-step work for customers.

Attribute usage to your workspaces, users, and projects while keeping MIOSA behind your own product.

Promote generated work from a sandbox into versioned deployments, rollbackable releases, custom domains, or Docker Deploy.

Product primitives

Primitive	Use it for	Not for
Sandbox	Code, files, commands, package installs, builds, tests, generated artifacts, preview servers	GUI/browser interaction
Computer	Browser automation, screenshots, mouse/keyboard, desktop apps, visual QA, user handoff	Headless code builds that do not need a desktop
Deployment	Stable public app/site/API URLs, versions, releases, rollback	Active development iteration
Docker Deploy	Many small customer apps running as containers inside one workspace appliance VM	Generic sandbox development
Artifacts	Files created by agents: PDFs, docs, images, reports, exports, screenshots	Hidden temporary state
Events	Real-time UI updates: tool calls, stdout, file writes, preview readiness, deployment status	Long-term storage

Build an agent-company product

Several modern AI products package the same high-level pattern: an agent, a persistent workspace, connected apps, a browser or cloud device, jobs, files, and generated deliverables. MIOSA exposes those primitives directly so you can build your own version inside your product.

Your product UI
  -> agent configuration
  -> device policy
  -> sandbox/computer/local device
  -> connected accounts and managed connectors
  -> streamed tool calls and events
  -> files, artifacts, previews, jobs, and deployments

Product shape	MIOSA primitives
Always-on virtual device	Persistent sandbox or computer, primary device policy, snapshots, jobs
Agent team	Orchestrator, child sandboxes, child computers, shared artifacts, event stream
Mini-app builder	Next.js sandbox, preview URL, artifact panel, publish flow
Cofounder/operator agent	Browser computer, connected apps, schedules, approvals, memory
Design/research agent	Web research, Refero managed connector, files, browser screenshots
24/7 monitoring job	Scheduled trigger, persistent device, app connector, notifications

The important architecture point is that the agent should work in a MIOSA device. The local CLI, MCP client, or your backend orchestrates, but files, commands, browser actions, previews, and artifacts live in the assigned runtime.

See What MIOSA agents can do, Agent devices, and Agent orchestration for the full model.

Example: generated app builder

Your frontend can show the same work stream users expect from modern AI builders: tool calls, command output, generated files, preview links, screenshots, and final deployment URLs.

Example: artifact workspace

Use a sandbox when the output is a file, not a deployed app.

User asks for a proposal
  -> agent writes Python or Node generator
  -> sandbox creates proposal.html
  -> sandbox renders proposal.pdf
  -> sandbox creates proposal.docx
  -> product shows artifacts in a side panel
  -> user downloads or asks for edits

Good artifact outputs:

client proposals
pitch decks
data reports
PDF invoices
SVG diagrams
PNG charts
HTML previews
ZIP bundles
code archives

Example: browser plus code workflow

Use both runtimes when the work crosses code and browser verification.

Sandbox agent:
  writes Next.js files
  starts dev server
  exposes preview

Computer agent:
  opens preview URL
  takes screenshots
  clicks through signup
  reports visual or functional issues

Sandbox agent:
  patches code
  reruns tests
  publishes

This is how you build products that feel like a coding agent, QA agent, and deployment agent are working together.

Sandbox templates should map to product jobs

Template	Intended job
`miosa-sandbox`	General Python and Node command execution
`nextjs`	Generated web apps, landing pages, dashboards, previews
`python-data`	Reports, charts, files, analysis, automation
`browser-agent`	Browser testing and Playwright-style automation
`artifact-lab`	PDF, DOCX, image, diagram, and HTML artifact generation
`agent-node`	Node agent runtimes, AI SDK harnesses, MCP clients
`agent-python`	Python agent runtimes, data tools, backend orchestration
`postgres-15`	Managed database or database-backed generated apps

Some templates may be private or roadmap depending on your account. The product shape should remain the same: choose the template that matches the job, then let the agent work inside the sandbox.

What to show users in your product

Users should not have to understand VM internals. Show them useful work:

current task and plan
tool calls
command output
files created or edited
artifacts generated
preview URL
screenshots from browser checks
deployment URL
rollback/version history
credits and runtime usage

The message should be: “Your agent is working in a real cloud workspace. You can watch it, inspect the files, preview the result, and ship when ready.”