Action Spaces

Series: Building Auditable LLM Systems · Part 7 of 11 Begin the design of an LLM decision system with its action space. Before designing prompts, tools, memory, or workflow logic, answer a simpler question: What is the model actually allowed to change? Once that boundary is fixed, the rest of the architecture can be derived from it: What may the model change? -> What must it know to choose that action? -> How can the system verify that the action follows from that evidence? -> How is the model prevented from taking other actions? -> Who validates and executes the proposal? -> How are consequences returned as feedback? Schemas, prompts, tools, workflows, and memory all serve this chain from information to action and consequence. ...

Series: Building Auditable LLM Systems · Part 11 of 11 A better result from a stronger planner or teacher is not automatically a valid LLM training target. A result is eligible for training only when it passes two gates: The model can observe the information on which the result depends. The model is authorized and able to execute the corresponding action. Otherwise the result can expose an information gap, an action-space gap, or a hindsight upper bound. It should not be presented as an answer the model ought to have learned. ...