AI Agents vs LLMs: Choosing the Right Tool for AI Tasks

Large language models have changed how software teams think about automation, reasoning, and intelligence. Almost overnight, tasks that once required brittle rules or custom ML pipelines became promptable. But as adoption has grown, so has confusion. Teams now ask a new question that did not exist a few years ago: should we use a large language model directly, or should we build an AI agent around it? This distinction matters more than it seems. I have seen teams over-engineer agentic systems for problems that only needed a single LLM call. I have also seen teams struggle with fragile prompt chains when what they really needed was planning, memory, and tool orchestration.