ActionEngine: From Reactive to Programmatic GUI Agents via State Machine Memory

TL;DR

ActionEngine introduces a novel two-agent framework for GUI automation that significantly improves efficiency and accuracy by using programmatic planning and persistent memory, reducing costs and latency compared to traditional vision-based methods.

Contribution

The paper presents a training-free, two-agent architecture that constructs a persistent GUI state machine and synthesizes executable programs, enhancing robustness and scalability in GUI agents.

Findings

Achieves 95% task success on Reddit tasks from WebArena.

Reduces cost by 11.8x compared to vision-only baseline.

Cuts end-to-end latency by 2x.

Abstract

Existing Graphical User Interface (GUI) agents operate through step-by-step calls to vision language models--taking a screenshot, reasoning about the next action, executing it, then repeating on the new page--resulting in high costs and latency that scale with the number of reasoning steps, and limited accuracy due to no persistent memory of previously visited pages. We propose ActionEngine, a training-free framework that transitions from reactive execution to programmatic planning through a novel two-agent architecture: a Crawling Agent that constructs an updatable state-machine memory of the GUIs through offline exploration, and an Execution Agent that leverages this memory to synthesize complete, executable Python programs for online task execution. To ensure robustness against evolving interfaces, execution failures trigger a vision-based re-grounding fallback that repairs the failed action and updates the memory. This design drastically improves both efficiency and accuracy: on Reddit tasks from the WebArena benchmark, our agent achieves 95% task success with on average a single LLM call, compared to 66% for the strongest vision-only baseline, while reducing cost by 11.8x and end-to-end latency by 2x. Together, these components yield scalable and reliable GUI interaction by combining global programmatic planning, crawler-validated action templates, and node-level execution with localized validation and repair.