Most organisations do not have an AI agent problem. They have an AI agent sprawl problem: a pilot here, a copilot there, three teams each wiring their own tools, and nobody able to say what any of it is permitted to touch or whether it still works after the last model update.
Status: product concept, in active development. This is something we are building, not something you can buy today. We publish our roadmap because the engineering thinking behind it is the useful part — and because we would rather show you the design than imply a finished product.
AgentOS is our answer — the shared platform underneath the agents we build, rather than another one-off agent. It is designed to be the place where an agent is designed, given tools, given policy, evaluated, released, and then watched in production.
What It Is Designed To Do
- Agent and workflow design — define what an agent does and the steps it may take, in one place rather than scattered across prompts.
- Model routing and provider abstraction — choose the model per task, and change that decision later without rewriting the agent.
- MCP and tool registry — a governed catalogue of what tools exist and which agents may call them.
- RAG collections and source governance — control what an agent may retrieve from, with permissions respected at query time.
- Prompt, skill and configuration versioning — changes are versioned artifacts, not edits someone made on a Tuesday.
- Policy and approval engine — consequential actions route to a human by rule, not by convention.
- Evaluation and release gates — an agent does not ship because it demoed well; it ships because it passed its test set.
- Execution traces, cost and latency observability — what the agent did, what it spent, and how long it took.
Why Build a Platform Instead of More Agents
Because the second agent is where the cost shows up. The first one is a project; the fifth one is an operations problem — five sets of credentials, five evaluation approaches, five ways of finding out something broke. A platform makes governance the default rather than something each project reinvents.
This is the same discipline we already apply to individual builds: scoped permissions, evaluation before rollout, and human approval where it matters. AgentOS is that discipline made reusable. It draws on the same foundations as our MCP development, RAG and evaluation work.
Tell us if this matches a problem you have — early input shapes what we build first, and we will give you an honest view of where it stands.