Look at how people actually work with AI today. They copy context between tools. They rewrite the same instructions. They maintain a handoff file by hand, because when Claude Code finishes a task, Codex doesn’t know it happened. They decide, every time, which agent should do which work — and brief it like it’s the first conversation.
That is an orchestration layer. It is running in your head.
There is an invisible cost to this. Nobody quantifies it and everybody pays it — not in tokens or API calls, but in human attention spent coordinating machines that were supposed to save attention. We call it the coordination tax.
Pardx doesn’t add another AI to the pile. It sits between you and all of them.
One memory, so a session that ends doesn’t erase what was learned. One approval layer, so there is a structured moment before anything consequential happens — and a record after. One interface, so the question “which AI did you use?” can start to disappear the way “which server did you SSH into?” did.
The engines stay. They keep getting better, and we have no side in that race — no vendor will coordinate a competitor’s agent as a first-class citizen, which is exactly why the coordination layer has to be independent. We don’t have a preferred engine. We have a preferred outcome.
We keep our claims to what is built: agents dispatched to your machines or your own private VM, approvals before writes, a memory that carries context across engines, a record of everything that ran. The rest we will earn.
We think the future is simpler than today: many models and machines, working as one — with you in control. We are starting there, with developers.