1. From Answer to Outcome
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An introductory chapter in a series called 'Agents Unpacked' that explains the conceptual shift from AI chatbots to AI agents. It contrasts chatbots (which answer questions) with agents (which pursue goals through an observe-think-act-repeat loop). Key concepts covered include the agentic loop, tools as the agent's 'hands,' memory across sessions, and a concrete travel-planning example illustrating the difference between getting an answer and achieving an outcome. The series targets developers familiar with LLMs who want to understand agentic AI architecture, tradeoffs, and practical applications.
Table of contents
The Half-Done FeelingWhat Changes When AI Can ActThe Loop UnderneathTools Are the HandsKeeping TrackA Concrete ExampleThe Key TransitionWhat This Series Will DoSort: