This episode explores the evolution of Glean, a workplace search and knowledge discovery company, from an enterprise search platform to one incorporating reasoning agents powered by large language models (LLMs). Against the backdrop of increasingly sophisticated LLMs, the discussion pivots to the challenges of integrating these models with an organization's internal data. More significantly, the conversation highlights the crucial role of "context injection"—effectively integrating company-specific knowledge with the LLM's existing "world knowledge"—to ensure accurate and relevant results. For instance, the hosts discuss how a powerful model without access to the right data is less effective than a less powerful model with access to relevant company information. As the discussion progresses, the definition of a "reasoning agent" is examined, contrasting it with Retrieval Augmented Generation (RAG) and fixed workflows. The hosts delve into the complexities of managing unbounded execution, debugging multi-agent systems, and ensuring the accuracy of agent outputs. Ultimately, the episode emphasizes the importance of robust evaluation frameworks and a blend of automated and qualitative assessment methods for building effective and reliable reasoning agents, reflecting emerging industry patterns in the development of agentic AI.
