The AI copilot that's
actually yours.
You describe what you want in plain English. How the agent behaves — system prompt, model selection, tool policy, retries, when to pause, when to branch — is a file you wrote, not a binary we shipped. Edit it, version it in git, port it anywhere the engine runs.
Installs contenox. Run contenox init to scaffold a workspace.
One command, new capability
The agent told you it can't search. So you gave it search.
MCP servers, OpenAPI services, shell tools — anything you can reach is a capability you wire in. No vendor update, no code change. One command and the same prompt works differently.
Every tool the agent can call is a registration you made. Connect your first MCP server →
What you can author
Glue work, with the policy on your side.
- →Someone yelled at you on Teams about a bug. Pipe the messages in; the chain checks the issue tracker for a duplicate and files it if not — pausing where you wrote it should pause.
- →It's Friday and you forgot the timesheet. Your git log knows what you did this week. The chain you wrote drafts the entries — your rounding rules, your project mapping, your approval gate.
- →New app on localhost:3000. You promised someone documentation. Playwright drives it; the chain decides what gets captured and where it lands. Notion, file, draft for review — whatever you authored.
- →Write the chain once, version it in git. Review each other's chains in PRs. The same artifact engineering already reviews; now your AI behavior lives there too.
For teams and orgs
When AI use crosses individuals, the shape of the work changes.
Peer-review each other's chains in PRs. Share MCP and tool registries. Audit trails per user. Policies you can actually enforce — written, versioned, the same shape as the chains they govern. Contenox for Teams adds that layer to the same engine the CLI runs on. Some of it doesn't fit on a laptop — Postgres, NATS, Vald, a worker. Self-host the stack, or let Contenox Services run it. Coming soon.