Most conversations about AI agents start with behavior.
The agent writes code, opens a pull request, updates a ticket, stores memory, calls an MCP server, or moves a task through a workflow. At least, that is usually where the discussion begins.
The behavior matters.
But there is a layer before behavior that is easy to skip: how the team itself is described before anything runs.
That description has to answer practical questions about participants, roles, allowed actions, approval points, context sources, memory scopes, and handoffs between implementation, validation, review, and documentation.
