The word "agent" has been attached to almost everything, which has made it close to meaningless in marketing material. The distinction that actually matters is simple: a chatbot produces text, an automation follows a fixed path, and an agent decides what to do next and then does it.
Takes input, returns text. It cannot act on anything. Useful for answering, useless for doing.
Follows a path you defined. When X happens, do Y, then Z. Reliable and predictable, and it breaks on anything you did not anticipate.
Has a goal and a set of tools. It decides which to use, observes the result, and continues until the goal is met or it gives up. The path is not fixed in advance.
Take handling an inbound enquiry.
| Approach | What happens |
|---|---|
| Chatbot | Answers the question in the message, and nothing else. |
| Automation | Sends a fixed acknowledgement, creates a CRM record, notifies you. Always the same steps. |
| Agent | Reads the enquiry, looks the company up, checks whether they are an existing customer, decides whether it is a quote request or a support issue, drafts an appropriate response, schedules a follow-up — and escalates to you when it hits something it cannot judge. |
This matters more than the capability list, because agent failures are less obvious than automation failures:
An automation follows a path you defined in advance, which makes it predictable and brittle. An agent decides its own path using tools available to it, which makes it flexible and less predictable. If you can write down the steps, build an automation — it will be cheaper, faster and easier to debug.
For well-scoped tasks with a human reviewing consequential actions, yes. For unsupervised work with real consequences, be careful — they fail in ways that look like success, and errors compound inside loops. The common pattern that works is an agent that prepares and a person who approves.
It depends on how many steps it takes, which varies per task and is not fully predictable. That is a genuine budgeting difference from fixed automations. Running the model locally removes per-step cost entirely, which is one reason agent-style work suits local deployment — see running AI locally.
Most businesses need automation and are being sold agents. If you can write the steps down, you do not need something that decides its own. Agents earn their complexity when the next step genuinely depends on what the previous one found.
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