Now that you know what your agent will do and where it’ll live, it’s time to ask an important question.
Does this problem actually need an agent?
Not every project needs an agent; not even every automation project. Sometimes a simple workflow, script, or rule-based process will do the job better. Understanding the difference saves time, money, and confusion down the road.
An AI agent is designed to do more than execute instructions. It reads, writes, and speaks in natural language. It interprets intent, applies knowledge, and makes decisions in uncertain situations. That flexibility is what makes it powerful, but it also adds complexity. Every LLM call costs tokens, time, and risk. So it only makes sense to use an agent when the work requires genuine reasoning or language understanding.
If the task is deterministic (meaning it has one predictable input and one predictable output) a workflow or automation tool is usually better. (here’s a quick tip: Botpress does both of these!)
If the task involves context, interpretation, or conversation, an AI agent is the right choice.
Let’s look at how Terminal Roast approaches this.
Taryn’s team has defined their first task: collecting customer feedback on new coffee flavors and recipes. Adrian, the lead barista, suggests two ways the agent could handle this.
The first is conversational. The agent asks why a customer liked or disliked a drink and tries to understand their reasoning. That requires nuance, interpretation, and memory. It’s a good fit for an agent.
The second is programmatic. The agent presents a simple list of new recipes and asks customers to respond with “yes” or “no.” That interaction doesn’t require reasoning. It’s a perfect candidate for automation.
By separating these two flows, the team makes the system more efficient. They use an agent where language understanding matters, and a workflow where logic alone is enough.
Many teams blur this distinction. They try to build an agent that does everything: answering questions, pulling data, updating records, and managing transactions. But every additional function increases cost, latency, and maintenance overhead. A better approach is to combine agents and workflows.
Use workflows for structure and reliability. Use agents for intelligence and adaptability.
Think of the agent as the “frontline” interface that understands users, then passes structured actions to automation systems that execute them precisely. When these two systems are clearly defined, the result is more stable, scalable, and transparent.
Here’s a quick test.
Ask yourself: does this task require understanding natural language, or could it be handled by a simple rule set? If it requires understanding — for example, identifying intent, handling ambiguity, or referencing knowledge — it needs an agent. If not, build it as a workflow.
You can always layer an agent on top later once you have clear logic and data in place. It’s so much easier to expand a working workflow into an intelligent one than to debug a complex, overbuilt agent.
Terminal Roast’s approach (separating conversational and programmatic tasks) is a good model. They end up with a leaner system that does exactly what each part is best suited for.
Clarity here pays off later in maintainability, cost, and performance.
Action: Review your proposed agent’s tasks and identify which ones require genuine language understanding.
Keep only those under the agent’s control, and plan to automate the rest through standard workflows.
