Accelerate agent adoption, the moment your team needs it.
Cartesian is the adoption layer for your company’s agents. It matches the task someone is working on to an approved agent and offers it inside Claude, ChatGPT and Copilot, while they work.
The same approved agents, whichever tool your teams work in.
The team that tested your agents already uses them. But how do you get the rest of the organisation using them?
Cartesian reads the conversation in Claude, ChatGPT or Copilot, works out the task, and finds the approved agent in your catalogue that does it. Nobody needs to know the agent’s name.
They run it or pass, and both are logged to their team. The agents run in order, and two years of behavioural research decide which agent gets offered and when.
Every team uses your approved agents from the day they ship, nobody rebuilds one that already exists, and you see the return as it happens. Join our design partner program.
Your agent ecosystem,
organised around your people and their work.
Cartesian does the organising. It packages your approved agents and puts them to use wherever your people work.
Cartesian matches the task someone is working on to an approved agent and offers it inside the tool they’re using, whether or not they knew the agent existed.
Your own agents and the vetted third-party ones, each bundled with the agents it depends on and published once.
The agents run in order inside your own cloud account, and every recommendation and every run is logged to the team that used it.
Adoption that keeps up
as agents, models and teams change.
New versions ship. Old ones get retired. What was approved last month isn’t always what’s running today.
Swap the model and what the agent can do changes with it. The catalogue has to know that happened.
A new standard, a new tool, a team that just got reorganised. The match has to keep up.
Someone starts this week with no idea what exists, and nobody reads an announcement from six months ago.
Cartesian offers the agent every time the work comes up, whenever that is.
Your tools build, govern and index an agent,
but that’s where they stop.
Vendor positions as published April to August 2026. Cartesian reads a registry. It does not replace one.
Security and control, built in.
Runs inside your own cloud account, and reads the conversation there to make the match.
Independently audited to SOC 2.
Your agent code, your prompts, and your agent activity stay in your environment.
Your identity provider decides who can call what.
The return when your agents are in use from day one.
Adoption at speed
New agents in use across every team the day they’re approved.
People get the agent while they’re doing the work, so nobody waits for the next training session or goes looking for the guide.
Inventory
Every approved agent, where it is, who can call it.
When your regulator or your board asks what AI you are running, you answer from the catalogue.
Reuse
One published agent serves every team.
Teams stop rebuilding an agent that already exists, so there are fewer builds and fewer fixes.
Measure impact
Every offer, every run, and every gap.
You see what got used, what actually solved the task, and what people asked for that no agent covers.
Illustrative scenario.
Agents rely on agents.
Package the whole thing.
Underneath, Cartesian is a package manager, for the people who build and publish the agents.
Example agent chain: a report agent calls a data agent for the numbers and a QA agent to check them, and they have to run in that order.
Package the three agents as one, declare the dependencies, and publish it to your catalogue. Every team runs the approved chain from the client tools they work in.
Third-party agents you have approved sit in the same catalogue, so nobody wastes time building what an existing approved agent already does.
Every team runs the approved chain.
Illustrative scenario. Nothing here is a customer result.
See adoption as it happens, and what it’s worth.
See usage against real demand, across every team. Cartesian logs every agent run the moment it happens.
Key reporting data includes cost per solved task, usage gaps, churn reports, and user-led agent requests.
Every step of every run, including the ones that failed: tokens, tool calls, and the model it escalated to.
It isn’t the agent saying it finished, it’s an external check that passes, or the user who accepts the result and does not come back.
The baseline you agree before anything is configured, measured again over the same window later.
Illustrative figures. Run counts alone do not prove business value.
Start with one team,
and one agent it already needs.
Whether you’re deploying agents or building them, this is where you start.