u/JustProcedure4155

I’ve been experimenting with separating “planning” from “execution” in computer-use agents.

Instead of letting a frontier model handle every tiny UI action directly, I split it into:

• Codex 5.5 → planning / judgment
• Codex Spark → actual execution

The surprising part is how much better this feels in practice.

A lot of computer-use workflows don’t actually need expensive reasoning for every step:

• scrolling
• clicking buttons
• navigating pages
• repetitive UI interactions
• QA loops
• E2E-style testing

Those tasks mostly need:

• speed
• determinism
• low latency

The frontier model is mainly useful for:

• deciding what to do
• recovering from failures
• understanding intent
• planning multi-step workflows

So instead of burning tokens on tiny execution details, I let Spark handle the execution layer.

Current experiments:

• automated QA
• browser-driven implementation
• E2E testing
• repetitive UI workflows
• computer-use orchestration

I recorded comparison videos between:

1. direct Codex 5.5 computer use (It may look frozen, but GPT-5.5 is actually thinking very hard.)

https://reddit.com/link/1t7vme0/video/sb29uu01q10h1/player

2. planner/executor split with Spark

https://reddit.com/link/1t7vme0/video/89yhsan3q10h1/player

The speed difference is pretty interesting.

Repo:
https://github.com/KingGyuSuh/awesome-codex-spark