## Introduction

In the domain of **agentic AI systems**, developers can choose between two broad architectural approaches for large language model (LLM) applications: **workflow-driven systems** and **autonomous agent systems**. Both approaches involve LLMs possibly using tools and external data, but they differ fundamentally in how the process is structured and controlled. This report provides a technical comparison of AI agents and workflows, drawing on insights from Anthropic’s *Building Effective Agents* guide. We will define each paradigm and then contrast them across key dimensions – **Purpose & Design Intent, Architecture & Execution Patterns, Flexibility & Autonomy, Use Cases, Development Complexity,** and **Observability & Control** – to help agentic AI developers choose the right approach for their needs.