A complete guide to agentic reasoning

This post provides a comprehensive overview of agentic reasoning, defining it as an AI approach that plans, acts, evaluates, and adapts towards a goal. It details the key components (LLMs, tools like web search, code execution, structured memory) and the reasoning loop (task decomposition, delegation, observation, synthesis, adaptation). It introduces agentic reasoning strategies such as Chain-of-Thought, ReAct, Self-reflection, and Multiagent Orchestration, and explains how Glean's Agentic Engine utilizes these for planning, retrieval, generation, and self-reflection, citing a 24% increase in response relevance. The post also discusses real-world applications, benefits, challenges, and getting started with agentic reasoning.