The author proposes a hierarchical model for scaling autonomous AI systems, moving from individual agents to AI organizations and AI holdings. This approach addresses the limitations of single-agent setups, where mixing different functions like marketing, analytics, and development often leads to performance issues and difficulties in testing specific tasks.
The transition begins by breaking down workflows into fixed sequences of roles, each with defined inputs, outputs, and responsibilities. For instance, in an automated testing workflow, the system uses distinct roles for requirement analysis, code implementation, and quality assurance. This structure allows for isolated testing and configuration of each step without affecting the entire process.
The next level involves a team of roles managed by a coordinator. This setup improves control, as individual roles can be adjusted independently. The author references external practices, such as the work of Loren Tan and Colin Matthews on cloud agents and the 'ai-factory' repository, which utilizes sub-agents for planning, implementation, and code review.
Scaling to an AI organization involves managing multiple functions around a specific direction, such as content creation and site maintenance. The final stage, the AI holding, is designed to oversee multiple organizations, select development directions, and allocate limited resources between them, separating high-level strategy from engineering execution.
