The article clarifies common misconceptions surrounding workflows, agents, and multi-agent systems in the context of AI applications. It emphasizes that not every large language model (LLM) application qualifies as an agent; the distinction lies in autonomy. Workflows are controlled by the user, while agents operate independently based on the goals set for them. Tools, such as APIs or databases, differ from agents, as tools provide specific functions that agents decide how and when to use. Misunderstanding these terms can lead to unnecessary complexity in solutions. The author introduces a spectrum of architectural choices, from simple workflows to complex multi-agent systems. Workflows are ideal for predictable and stable processes, such as handling support tickets, where the sequence of steps remains constant. In contrast, single agents with tools are better suited for tasks that require adaptability, like marketing content generation, where decisions depend on previous outcomes. The article highlights that as the number of tools increases, managing context becomes more challenging, often necessitating a shift to multi-agent systems to maintain efficiency and clarity. Multi-agent systems are only justified under specific conditions: when tasks can run in parallel, when context overload degrades performance, or when separation for security or compliance is necessary. The author warns against overengineering by stressing that simpler systems often yield better results. The main takeaway is that starting with workflows or a single agent can save time and resources, and complexity should only be adopted when absolutely needed.