On August 20, 2026, Anything launched Skydive. The pitch is unusually literal: you are not buying chatbots or workflows, you are hiring coworkers. You create an agent, give it a name and a role, and it gets its own cloud computer with a browser, a file system, and a terminal. Then it works where your team already talks, in Slack, email, and iMessage.
If you have been running agents in production, your first reaction was probably ours too: this looks a lot like where the whole category is going. It does. Skydive gets the big things right, and of everything we have compared Vybe against, it is the closest in architecture. So this comparison is less about who has the better idea and more about two honest questions: how much do you want to build yourself, and how much do you need working today?
What Skydive actually is
Skydive is Anything's platform for standing up a fleet of AI coworkers. Every agent runs on an isolated machine of its own and can write code, provision a Postgres database, host a web app, browse the web, and install its own tools. Agents are model-agnostic, drawing on more than 170 frontier and open-weight models. They live in Slack, email, and iMessage, and you can also reach them through a CLI or an API. Administrators control access, data, usage, and spend from one place while individual employees build their own agents.
Anything runs its own company on it: a 15-person team working alongside more than 150 agents across support, engineering, marketing, and operations. That is a genuinely impressive proof point, and it tells you who Skydive is built for. This is a platform for a company that wants to add capacity across many functions at once and govern the bill centrally.
Where Vybe and Skydive agree
The common ground here is bigger than in any comparison we have written, so it is worth stating plainly:
- Each agent gets its own computer. Both give every agent an isolated sandbox with real compute, a file system, and the ability to run code and stand up databases. Neither is a thin chat wrapper.
- Both are model-agnostic. They route across many models instead of welding you to a single lab, so a better model next month is an upgrade, not a migration.
- Both live where you work. They operate natively in Slack and over email, so the agent meets the work instead of forcing everything into one console.
- Both build and operate, not just answer. They produce durable software and run it, rather than posting a reply and forgetting it.
- Both are async and proactive. Each can take a goal, work it over hours or days, and follow up without being babysat.
If your mental model is "an autonomous agent with its own computer that lives in Slack," the two genuinely overlap. The daylight shows up around how you get there and what you need in place first.
Where Vybe and Skydive part ways
1. Deploy a specialist vs build a coworker from scratch
Skydive hands you a general-purpose agent and a naming box. You define the role, connect the tools, and shape the agent until it behaves the way you want. That flexibility is powerful if you have the time and the appetite to build.
Vybe starts you much further down the road. The gallery has more than 30 named specialists that already know their job. Ashley Belfort preps your calls, takes notes, and updates the CRM. Carolyn screens candidates and drafts outreach. Darin stands up a deal board, onboards your reps, and sends a daily digest that flags deals with missing data. Each one ships with its integrations and workflow already wired. You deploy it and adapt it, rather than teaching a blank agent what good looks like for every function from zero.
2. Governance that ships built-in vs a fleet you have to police
Giving an agent its own browser, file system, and terminal is what makes it capable. It is also exactly the kind of power that needs oversight. Anything's answer is central admin control, which is the right instinct, but it is also a new job somebody at your company owns from day one.
Vybe's answer is built into the architecture. Every agent runs isolated and scoped to only what you grant it, with granular role-based access, SSO, and SOC 2 Type II compliance already in place. The controls a security team asks about are there before you deploy, not something you assemble around a fast-moving fleet.
3. Proven with customers vs launched this week
Skydive is days old at the time of writing. The internal results at Anything are real, but running agents on your own company is different from being battle-tested across other people's messy stacks. Vybe has been in production with paying teams. UpKeep put Vybe agents into real workflows and shipped. If you are handing agents work that touches revenue or customers, a track record outside the vendor's own walls is worth weighing.
4. Right for a lean team today vs a platform built around fleets
Skydive's unit of value is a fleet: many agents across many functions, with an admin watching the spend. That is the right shape for a company adding headcount-equivalent capacity everywhere at once. It is a lot of setup if your actual problem is smaller.
Vybe scales down as cleanly as it scales up. A single operator at a ten-person startup can deploy one specialist and get value the same afternoon, without provisioning a fleet or standing up a governance function to supervise it. You can grow into more agents when the work demands it, instead of committing to the fleet model on day one.
Vybe vs Skydive at a glance
| Vybe | Skydive | |
|---|---|---|
| Agent compute | Isolated sandbox per agent | Isolated computer per agent |
| Models | Routed across many models | 170+ models |
| Where it works | Slack, email, web, your tools | Slack, email, iMessage, CLI, API |
| Getting started | Deploy a ready-made specialist | Build and train an agent from scratch |
| Governance | Per-agent scoping, RBAC, SSO, SOC 2 Type II | Central admin control of access and spend |
| Track record | In production with paying customers | Public launch August 2026 |
| Best fit | Lean teams and operators, scaling up | Companies staffing a full agent fleet |
When Skydive is the right call
Fairness matters, so here is the honest version. If you are a well-resourced company that wants to add agent capacity across many functions at once, you have someone who can own fleet governance, and you like the idea of building each coworker to your exact spec, Skydive is a serious, well-designed platform. The self-improving, git-backed memory model is genuinely interesting, and Anything running its own company on it is about the strongest dogfooding story you will find.
Vybe is the better fit when you want to move now rather than build: ready-made specialists instead of blank agents, enterprise controls and compliance already in place, and an entry point a small team can adopt without standing up a fleet. If you would rather deploy a working agent this week than staff and supervise a workforce of them, that is the difference.
How to build this on Vybe
Getting an agent running on Vybe takes minutes. Pick a specialist from the agent gallery or describe the job you want done. Connect it to your stack through Vybe's integrations, from Slack and Gmail to your CRM and billing tools, and the agent builds whatever app or workflow it needs to do the work. Then it runs that workflow on a schedule and reports back where your team already is.
If you are still mapping the field, our guide to the best AI agent platforms in 2026 and the breakdown of an AI app builder vs an AI agent platform are good next reads.
FAQ
Is Vybe a Skydive alternative?
Yes. Both give you autonomous agents that each run on their own computer, work across many models, and live in the tools your team already uses. Vybe differs by shipping ready-made specialists you deploy in minutes, enterprise controls and SOC 2 Type II compliance that are already in place, and an entry point that suits a small team, not only a full fleet.
How is Vybe different from Skydive?
The two are architecturally close. The practical differences are how you start and what you need in place: Vybe gives you a gallery of pre-built specialists and built-in governance today, while Skydive gives you general-purpose agents you build and train yourself, governed by central admin control.
Do I need a big team to use Vybe?
No. Vybe works for a single operator or a small team deploying one agent, and it scales up from there. Skydive is built around a fleet of agents with an admin overseeing usage and spend.
Is Vybe locked to one AI model?
No. Vybe routes to the best available model for each task. Skydive is also model-agnostic, supporting 170+ models, so on this point the two are aligned.
The bottom line
Skydive is a strong launch and the closest thing we have seen to Vybe's own bet: autonomous agents with real computers that live where you work and build durable software. On architecture, the two rhyme. The real question is what you need. Skydive asks you to build and staff a fleet, then govern it. Vybe hands you specialists that already know the job, controls that already satisfy a security review, and a track record with customers who put it into production. If you want to deploy this week instead of building for a quarter, start at vybe.build.
