

14-day trial. No DevOps. No Sales call. Provisioned in under a minute.
A year ago I had a working agent in a side project and no idea what it was doing.
It was a Claude Sonnet loop wired into a tool harness, given a brief, and pointed at our customer support inbox. It triaged tickets, drafted replies, and surfaced anything it wasn't confident about. The output quality was good, better than the keyword-rule bot it replaced, but the part I could not get used to was the silence. The agent ran. Tickets got drafted. I had no idea, on any given morning, what it had done overnight, what it had spent, or whether it had touched anything I would not have touched myself.
I asked around. Friends running similar agents in production all had the same setup: a cron job, a few Slack alerts wired to specific tool calls, and a private Notion page describing what the agent was "supposed" to do. Costs were a monthly surprise. Audit trails were grep over CloudWatch. Adding a second agent meant duplicating the cron, the Slack alerts, and the Notion page, and hoping the two agents did not stomp on each other.
That is the gap Agent Hive is built for. The agent layer is fine. The operating layer around the agents is missing.
Agent Hive treats an AI workforce the way a real company treats its human workforce.
The names matter less than the structure. The structure matters because it gives the human in charge the same affordances a real CEO has: an org chart, an issue tracker that lists every piece of in-flight work, an approvals queue for the risky stuff, a live cost rollup, and a way to fire an agent that is not performing.
We considered three other shapes before landing on this one.
A pure chat product, where you talk to one big agent and it does everything. The problem with this shape is that it does not survive growth. The moment a second agent is involved, you need a way to refer to them, hand off work, and review who did what. Chat is a terrible substrate for any of that.
An observability tool, where you bring your own agents and we just give you dashboards and traces. The problem here is that the parts of an agent operation that matter (approvals, budgets, tool permissions, hiring and firing) are not observability. They are operations. Putting them behind a "trace viewer" mental model is the wrong abstraction.
A framework, where we ship a library you embed in your codebase. We have nothing against frameworks; the Hive runtime that powers our agent loop is itself a framework. But frameworks are a developer-tools product. They do not solve the operating problem for the person actually running the business.
The current shape of Agent Hive is built for AI-native startups and the autonomous-business operators who are running them solo or as a small team. If you are the one person who needs a way to see, in 30 seconds, what your agents are doing today, that is the audience we are building for.
We are not yet the right tool for a 500-person enterprise that wants SSO, six layers of RBAC, and a procurement cycle. That product exists; that product is not yet us.
The Agent Hive launch covers six surfaces:
There is a roadmap behind that, and I will write about the pieces as they ship. The short version: this is the operating system for a small business with mostly-AI staff, written by a small team that runs one ourselves.
If any part of the above sounds like the thing you have been duct-taping together: come use it. The free tier is genuinely free, includes managed Claude credits, and a working CEO agent on day one. If something feels wrong (the abstractions, the IA, the copy) tell me. We are early enough that "this should work differently" is still the most useful kind of feedback.