Should the AI that decides what to click be a separate system from the one that finds the button?

This explores whether GUI agents work better when the surrounding system splits planning (deciding what to do next) from grounding (finding the right button or field on screen). The corpus says yes, and unusually consistently. AutoGLM's finding is that the two jobs have opposing optimization requirements, so bundling them in one policy makes them fight each other (Why do planning and grounding pull against each other in agents?). Putting an interface between them lets each be improved separately and still compose into a working agent. Agent S, AutoGLM and OmniParser reached this factoring independently, each with a language-centric Agent-Computer Interface in the middle (How should agents split planning from visual grounding?).

The evidence shows up at more than one seam. Agent S feeds the planner visual input for understanding the environment and gives the grounding step image-augmented accessibility trees. That separation produced a 9.37% gain over baseline, compared with forcing end-to-end prediction (Can structured interfaces help language models control GUIs better?). OmniParser found the same problem one level down. GPT-4V struggles when it must work out what an icon means and pick an action at the same moment, and pre-parsing the screenshot into labeled elements removes that double burden (Why do vision-only GUI agents struggle with screen interpretation?). Splitting the work is what unblocks the model in each case.

The corpus's wider harness notes explain why this happens at the harness level and not inside the model. Reliable agents seem to get their reliability by moving cognitive burdens (memory, skills, interaction protocols) out of the model and into structure around it (Where does agent reliability actually come from?). Harness-only changes lift frozen models without touching weights (Can execution harnesses lift model performance without retuning weights?). Better organization can also let a weaker planner match a stronger one's code localization (Can explicit behavior maps help weaker planners compete with stronger models?). These notes aren't about GUIs, but they suggest a planning/grounding split is one instance of a general pattern. That pattern is to stop asking the model to solve, in a single pass, a problem the system can pre-structure.

Two things complicate the yes. First, the split isn't always needed. When an application exposes or can be made to expose an API, API-first agents cut task time by 65–70% at 97–98% accuracy, because they skip grounding altogether (Can API-first agents outperform UI-based agent interaction?). The split earns its keep where the GUI is the only door. Second, harness benefits depend on the model. Ability to write good harness edits is flat across model tiers, but ability to benefit from them peaks in mid-tier models. Weak models fail to invoke the harness, and strong ones struggle to follow it faithfully (Do stronger models always evolve harnesses better?). So a planning/grounding interface is likely to pay off most for mid-capability models, though the corpus doesn't test that for GUI agents specifically.

The corpus doesn't show a head-to-head where a single, very strong model, trained end-to-end, beats the split design. It shows the split winning against raw end-to-end baselines. Whether the split stays necessary as models improve is still open.