General-Purpose AI Agent Robots
A general-purpose AI Agent robot uses a single reasoning AI Agent across many tasks rather than a task-specific program. That generality is the appeal: the same AI Agent can read a new goal and a new scene. The limit is that a general AI Agent is stateless by default, so what it learned on one task does not carry to the next.
What generality buys
A general AI Agent does not need to be rebuilt for a new task. It reads the scene and the goal, decides on a subgoal and a tool, and acts. That removes the per-task engineering that dominates classical automation, where each new object tends to mean a new program.
What it does not buy
Generality of reasoning is not retained capability. Ask the same AI Agent to repeat yesterday's task and it re-derives the same solution from scratch, at the same cost, with the same chance of the same failure. Nothing about having solved it once makes it easier the second time.
That is a specific and fixable limitation, and it is distinct from the model's competence. The AI Agent is not worse at the task; it simply has nowhere to keep the answer.
What would change it
Somewhere to put validated behaviour, with enough structure that it can be retrieved and checked rather than merely replayed; and somewhere to record why an attempt went the way it did. Both need to be inspectable, because a general AI Agent operating over time accumulates conditions no single demonstration covered.
How TGL relates
TGL supplies exactly those two places. Validated behaviours enter the Skill Library with a stated scope and an outcome test; conditions, outcomes, diagnoses and repairs enter Experience Memory. The AI Agent keeps its generality, and the second attempt at a task starts from what the first one established.