The best AI OS for your business, by type
AI OS names four different products: infrastructure for running AI on your own data platform, a research kernel for agent developers, one person's Claude Code setup, and a platform a whole team runs its work on. Pick the kind first, then compare the platforms in it by where people work with the agents, how the agents reach company data, and who signs off.
- 1
Infrastructure AI OS
Storage, data and an agent runtime on your own platform
VAST Data
- 2
Agent OS kernel
Schedules the LLM, memory and tools agents share
AIOS research project
- 3
Personal AI OS
One person's context, skills and tools
Claude Code, Codex
- 4
Business AI OS platform
A team's people and agents share context, tools and sign-off
Kylon, Dust, Glean, Copilot Studio, Relevance AI
Four things people call an AI OS
Search for AI OS and the results mix four kinds of product. They solve different problems, so the first question is which kind you need.
- Infrastructure AI OS. VAST Data describes its AI OS as a single unified infrastructure layer that brings storage, ingestion, analytics, vector search, streaming and an AI runtime together, with AgentEngine as the runtime for agents.
- Agent operating system kernel. The open research project agiresearch/AIOS embeds a large language model into an operating system abstraction. Its kernel manages the LLM, memory, storage and tools that agents share, and its SDK is for developers who build agents on top.
- Personal AI OS. One person's setup in Claude Code or Codex: CLAUDE.md instructions and auto memory, skills, MCP servers for their tools and subagents with their own permissions.
- Business AI OS platform. A workspace where a company's people and agents share context, connected tools and approvals, so the recurring work of a team runs there. Five of them are compared below.
Some lists also include device operating systems such as Google Fuchsia, which Google describes as a general-purpose open-source operating system, and Tesla's FSD (Supervised) driving software. That is a different meaning of the term, so they are left out here.
Which kind you need
| If you want to | Look at | Usually run by |
|---|---|---|
| Run AI workloads on your own data infrastructure | An infrastructure AI OS, such as VAST Data's | Platform and data engineering teams |
| Study how agents share an LLM, memory and tools | An agent OS kernel, such as AIOS | Agent developers and researchers |
| Run your own work with an assistant that knows your files | A personal AI OS in Claude Code or Codex | One operator |
| Run the business's recurring work with people and agents together | A business AI OS platform | Teams, and agencies that build for clients |
The kinds stack. A team platform can sit on top of any infrastructure, and a personal Claude Code setup can join a team platform as one of its agents. The agentic operating system guide explains the layers in more detail.
Five business AI OS platforms compared
Each cell comes from the platform's own pages, checked November 5, 2026. The rows describe how each one works, so you can match it to how your team works today.
| Platform | Where people and agents work | Company data and tools | Sign-off before actions | Claude Code, Codex and outside agents |
|---|---|---|---|---|
| Kylon | People and Agents are members of the same Rooms and threads, and anyone can @mention an Agent (multiplayer AI) | Connections granted to each Agent and each person separately | Emails, record changes and client-facing work come back as a draft with Approve and Edit | Claude Code and Codex join as Agents through Invite my agent and keep running on the owner's computer (how it works) |
| Dust | People and agents share company knowledge, tools, conversations and notifications (dust.tt) | No-code agents combine instructions, knowledge sources and tools, and Dust acts as an MCP client for internal systems (Dust blog) | High-stakes tools can be set to require user confirmation (Dust blog) | A Dust CLI skill teaches Claude Code or Codex to call Dust agents (Dust docs) |
| Glean | Agents and workflows on Glean's Work AI platform, triggered by events or routed between agents (glean.com) | Connected applications indexed for permission-enforced enterprise search (glean.com) | Write tools in interactive agent sessions pause for approval by default (Glean docs) | A Glean plugin brings company knowledge, tools and skills into Claude Code, Cursor and Codex (Glean docs) |
| Microsoft Copilot Studio | A low-code studio; agents work across Teams, Microsoft Copilot, websites and other channels (Microsoft Learn) | Prebuilt or custom connectors to organizational data and systems (Microsoft Learn) | Agent flows can include human review steps, with role-based access for administrators (Microsoft Learn) | Agent-to-agent connections hand tasks to agents hosted outside Copilot Studio (Microsoft Learn) |
| Relevance AI | Agents and multi-agent workforces, with a Task View for conversations, monitoring and approvals (Relevance AI docs) | Tools that call APIs, plus files, Google Drive, SharePoint, Notion or websites as knowledge (Relevance AI docs) | Each handoff is set to Auto Run, Approval Required or Let Agent Decide (Relevance AI docs) | A Claude Code plugin manages agents, tools and workforces from the terminal (Relevance AI docs) |
How to choose between them
- Start where the team already talks about the work. Requests, drafts and decisions should land where people read, whether that is Teams, a chat tool or a workspace like Kylon.
- Write down who signs off on what. For each job, list which actions can run on their own and which wait for a person, then check that the platform shows the source next to the draft.
- Count the agents you already run. If someone has an AI OS in Claude Code or Codex, decide whether that agent should take part in the team's work or call the platform's agents from the terminal.
- Grant access per agent. Give each agent only the data its job needs, and make sure you can take it back.
- Run one job end to end before comparing more. The how-to-build guide walks through the first one in seven steps.
Where Kylon fits
Kylon is a business AI OS platform built around the room: the team, Kylon Agents and the Claude Code or Codex agents people already run work in the same thread, with Connections granted per Agent and a person approving what leaves. Agencies use it to deliver one AI OS per client workspace. The AI OS overview shows each layer in the product.
Questions people ask
What is the best AI OS? It depends on the kind you need. For AI workloads on your own data platform, an infrastructure AI OS such as VAST Data's. For agent research, the AIOS kernel. For one person, a Claude Code or Codex setup. For a team running its recurring work with agents, a business AI OS platform such as Kylon, Dust, Glean, Microsoft Copilot Studio or Relevance AI.
Is Google Fuchsia an AI OS? Google describes Fuchsia as a general-purpose open-source operating system. It shows up in some AI OS lists, but it is a device operating system, not a platform where a business runs its work with agents.
Can I bring my Claude Code AI OS into a team platform? In Kylon, yes. Connect Claude Code or Codex as an Agent with Invite my agent. It keeps its skills and files on your computer, and the team can @mention it in a Room. See Claude Code AI OS for teams.