## Purpose and Design Intent

When deciding between a workflow or an agent, the **intended purpose** of the system is a primary consideration. Workflow-based systems are **designed for well-defined problems** where the solution procedure is largely understood in advance. The developer’s intent is to *codify the solution path explicitly* to ensure consistent outputs. Anthropic notes that workflows offer greater **predictability and consistency** on such well-scoped tasks. In other words, if you know how to solve the task step by step, you can program those steps and let the LLM fill in the specifics at each stage. This can yield higher reliability, as the LLM is guided through a structured process (for example, always summarizing before analyzing, or always validating an answer with a secondary check). The trade-off is that workflows lack flexibility beyond what the developer anticipated. They are not meant to be creative or to handle novel requirements; instead, they shine at **repeating a known procedure** with the added intelligence of an LLM at each step.

By contrast, the design intent behind an AI agent is to tackle **open-ended or complex tasks** that *cannot be neatly mapped out* in advance. Agents are intended to be **adaptive problem-solvers**. The developer provides the agent with tools, some initial prompting (e.g. instructions to reason and act), and broad constraints, but does **not hardcode the exact steps**. This design empowers the agent to handle scenarios where the number or type of steps required is unpredictable. For instance, an agent might decide to iteratively research a topic via multiple queries, synthesize the information, ask the user for clarification if needed, and write a report – all without the developer explicitly enumerating those sub-tasks. The purpose here is to leverage the **LLM’s own reasoning and planning abilities** to find a solution path. As Anthropic suggests, agents are the better option *“when flexibility and model-driven decision-making are needed at scale”*. In summary, **workflows** are built with a *solution-first mindset* (you know how to solve it, you just implement the steps), whereas **agents** are built with a *problem-first mindset* (you have a goal, and you trust the AI to figure out the solution steps autonomously).

It’s important to note that more autonomy is not always better. The Anthropic guide emphasizes starting with the **simplest possible solution** and only increasing complexity to workflows or agents if needed. If a single prompt or a trivial chain can accomplish the task, that simplicity often outperforms a heavier agentic system. The added autonomy of an agent comes at the cost of more complex behavior, which can introduce new failure modes. Thus, the design intent should align with the problem requirements: use **workflows for straightforward tasks** where control is paramount, and reserve **agents for tasks requiring initiative and adaptability**.