Is it a fit?
The published fit and not-a-fit criteria, in full, and what the fit check gives you.
A good fit if
- Your agents outlive a single session and several agents or teams need one shared memory.
- A board, regulator, auditor or customer may ask why the AI decided something.
- Agents take actions that leave the organization, and approvals should depend on whether the action can be undone.
- You run more than a handful of agents and need to know which are held, which are stalled and which actually finished.
- Context must be checked against a schema before a model sees it, and retrieval must keep relationships between facts.
- Knowledge and credentials must stay inside your own tenant boundary.
- Outside AI clients and agents need to work with your knowledge over a standard protocol (MCP), scoped to your tenant.
Not a fit if
- You need a chatbot, a single model, or a thin wrapper around one.
- You are building one agent for one workflow and nobody will later ask why it acted.
- You want a public, unauthenticated data endpoint. OCTOPUS does not operate one (the public concierge answers questions about OCTOPUS only).
- You need self-serve signup today. Access is by request while early-access teams onboard.
The fit check
You give about 20 minutes and a plain description of one workflow where the same kinds of decisions come up again and again, with no member, customer or case identifiers. You get back a fit checklist in three parts, operations, IT and finance, filled in against the published fit and not-a-fit criteria with the reason for each box. You record the verdict. If it is not a fit, the team says so and says why.
About the public endpoints
The concierge on octo.ad, over MCP and REST, needs no account. It answers about OCTOPUS from public material only and exposes no customer data. That is the sense of 'no public, unauthenticated data endpoint' above.