Not Every AI System Needs to Be an Agent

This post explores recent advancements in Large Language Models (LLMs) and compares different AI system architectures, including pure LLMs, Retrieval Augmented Generation (RAG)-based systems, tool use & AI workflows, and AI agents. Using a resume-screening application as an example, it illustrates the capabilities and complexities of each architecture. The author argues that not every application requires an AI agent; the right architecture should be chosen based on needs. The post emphasizes the importance of building reliable AI systems, recommending starting with simple, composable patterns and incrementally adding complexity, prioritizing reliability over raw capability.