The planner works backwards from a goal that’s provided from a user's ASK.
We call this approach "goal-oriented AI" — harking back to the early days of AI when researchers aspired for computers to beat the world's reigning chess champion. That grand goal was achieved eventually, but with the unusual competence of new LLM AI models to provide step-by-step directions for practically any goal can be attainable when the right skills are available.
Because the planner has access to either a pre-defined library of pre-made skills and/or a dynamically defined set of skills it is able to fulfill an ASK with confidence. In addition, the planner calls upon memories to best situate the ASK's context and connectors to call APIs and to leverage other external capabilities.
The Jobs To Be Done (JTBD) movement has popularized a shift in moving from work outputs to work outcomes. Instead of focusing on the features or the functions of a product or a service, the JTBD approach emphasizes the goals and desires of the customer or the user, and the value or the benefit that they seek or expect from using the product or service. By understanding and articulating the JTBD of the customer or the user, a product or service can be designed and delivered more effectively. You just need to make the right ASK that isn't just "turn on the lights" and instead a more challenging goal like "I want a job promotion."
The planner will operate within the skills it has available. In the event that a desired skill does not exist, the planner can suggest you to create the skill. Or, depending upon the level of complexity the kernel can help you write the missing skill.