Teleodynamic AI can be framed as the co-evolution of model structure, parameters, and internal resource state. For engineering purposes, the useful questions are concrete: what resource is measured, what action consumes it, what outcome replenishes it, and what happens when the budget is exhausted?
Two timescales
A fast loop may perform inference or bounded parameter updates. A slower loop may choose a mode, prune memory, accept a structural change, or halt growth. These loops require explicit state and tests rather than metaphor alone.
No-op is a valid outcome
A resource-constrained system should be able to refuse, defer, select a smaller model, reduce context, or stop adaptation. The reason must be observable and reviewable.
Publication boundary
MiRust publishes this as research. It does not claim that the WordPress site or the separate GGUPF implementation currently exhibits teleodynamic learning.