Glean's technical white paper discusses the capabilities of AI agents that autonomously perform tasks for users by integrating with enterprise tools and workflows. It covers how Glean personalizes agent experiences, categorizes common enterprise queries, and evaluates agent performance using LLM-based techniques. The paper emphasizes the importance of effective search routing and the creation of tailored workflows within organizations.

LangChain has opted not to develop a visual workflow builder, allowing other platforms to fill this niche, as they believe true empowerment lies in enabling non-technical users to create agents rather than workflows. The article discusses the limitations of visual workflow builders, particularly their complexity and the challenges they present to users, while advocating for a focus on building no-code agents and improving code generation capabilities. The conclusion emphasizes the need for solutions that facilitate the creation of reliable agents without the complexities associated with workflows.

The article explores the evolution of AI system development from Large Language Models (LLMs) to Retrieval Augmented Generation (RAG), workflows, and AI Agents, using a resume-screening application as a case study. It emphasizes the importance of selecting the appropriate complexity for AI systems, focusing on reliability and the specific needs of the task rather than opting for advanced AI agents in every scenario.