AI Agent Robotic Arm
An AI Agent robotic arm combines a reasoning AI Agent — which decides what to do next — with the perception, grasping and motion components that let a physical arm do it. The design question is where to draw the line between the two, and what evidence crosses it.
The division of labour
The AI Agent handles what is expensive to do slowly: reading the situation, ordering subgoals, choosing a tool, recognising that the outcome contradicts the plan. The arm-side components handle what must be fast and physical: metric depth, collision-free motion, contact and high-rate control. Neither side can do the other's job, and trying to merge them produces a system that is either too slow to control or too shallow to plan.
What crosses the boundary
Evidence. The AI Agent's picture of the world is exactly what its tools report, so the interface determines what the AI Agent can reason about. If an executor reports only “command sent”, the AI Agent cannot tell a successful grasp from a failed one. If it reports the intended physical effect and whether that effect was observed, the AI Agent has something it can act on.
This is why TGL attaches an outcome test to every Skill Block rather than treating execution as assumed to succeed.
Why verification makes repair local
When each subgoal carries a test of its effect, a failure identifies a specific behaviour. The AI Agent can then re-observe, choose a different executor, or revise the remaining route — without re-deriving the whole task. A demonstration that fails at one stage does not invalidate the stages that were verified.
From execution to accumulation
The same structure is what makes a robotic arm accumulate capability rather than repeat episodes. Validated behaviours enter the Skill Library with their scopes; the conditions, outcomes, diagnoses and repairs enter Experience Memory. A later task retrieves both, so one task can make the next easier rather than merely leaving a log behind.