MAO is a multi-agent orchestration tool, and its launch line is unusually literal: “Team up Claude Code, Codex and Grok on one canvas.” That single sentence is most of what is publicly verifiable right now, so the honest way to cover it is to describe the category it belongs to and the questions that decide whether it works for you.
What “one canvas” implies
Running several coding agents in one workspace is a coordination problem, not a model problem. Each agent needs its own context, its own file access and its own budget, and a human needs to see what all of them are doing without reading four terminal windows. A canvas implies a shared visual layer where those sessions sit side by side and work gets handed between them.
That is a real gap. Most people today run one agent, hit a limit, and switch tools manually. A canvas is an attempt to make that switching explicit and visible instead of a habit.
Where this category earns its keep
Orchestration pays off in a narrow set of situations: parallel exploration of the same problem, one agent writing while another reviews, and long tasks where a single context window runs out. Those are the cases where a second agent adds something a bigger prompt cannot.
It pays off much less for ordinary feature work. If one agent can hold the task in context, adding a second one mostly adds merge conflicts and a new class of failure where two agents disagree and nobody notices.
Six questions to ask before adopting it
First, concurrency: do agents share a working tree or get isolated branches? Second, conflict handling: when two agents edit the same file, is the collision surfaced or silently resolved? Third, permissions: can each agent be scoped separately, or do they inherit one broad grant? Fourth, cost: is spend tracked per agent, and is there a hard stop? Fifth, persistence: what happens to a session when the app closes? Sixth, exit: can you export the transcript and the diff, or does the work live only inside the canvas?
Those six answers matter more than any feature list, because the failure modes of multi-agent work are almost all coordination failures.
A reasonable way to trial it
Pick a task with a real interface boundary, such as adding an endpoint and consuming it from a client. Assign one agent per side, and watch how the handoff happens. If you have to copy context between them by hand, the canvas is a viewer rather than an orchestrator. If conflicts surface with enough context to resolve in one pass, it is doing the job.
For more on how desktop agents behave when they are given file access, the guides index has the operational detail, and the FAQ covers the permission questions. If you want to run an agent locally rather than in a browser tab, start from the download page, and see how to use an AI agent on Windows for the setup path.