How can I build robust, enterprise-grade AI agents?
Towards Enterprise-Ready Computer Using Generalist Agent
This paper details the development of IBM's Computer Using Generalist Agent (CUGA), a multi-agent system designed to perform complex tasks within web applications. CUGA utilizes an iterative development process with automated evaluation and analysis tools to refine its architecture and improve performance on the WebArena benchmark. Key aspects relevant to LLM-based multi-agent systems include: a hierarchical agent architecture with a plan controller and sub-task execution agents; use of LangChain and LangGraph for LLM interaction and agent coordination; emphasis on context enrichment and knowledge injection to improve planner performance and mitigate LLM hallucinations; and a focus on iterative development, automated evaluation, and addressing real-world complexities in web applications. The research highlights the challenges and potential of building robust, enterprise-ready multi-agent systems using LLMs.