How can I build a scalable, fault-tolerant multi-agent system for Windows UI automation?
COLA: A SCALABLE MULTI-AGENT FRAMEWORK FOR WINDOWS UI TASK AUTOMATION
This paper introduces COLA, a multi-agent framework designed to automate Windows UI tasks using LLMs. COLA uses a dynamic, scenario-aware approach to task decomposition and agent selection, improving adaptability and scalability compared to static agent systems. Key features relevant to LLM-based multi-agent systems include a pool of specialized decision agents with distinct capabilities, a task scheduler for dynamic agent assignment, memory units for agent self-evolution based on past experiences, and an interactive backtracking mechanism enabling human intervention for non-destructive error correction. The framework demonstrates improved performance on the GAIA benchmark, particularly for tasks involving web browsing, a common requirement in UI automation.