The difference in one table
This compares the two kinds of tool in general, not any one product.
| An automation builder | An AI agent | |
|---|---|---|
| How you set it up | You draw the steps: when this happens, do that, then that. | You describe a role and a goal. The agent works out the steps each time. |
| Best at | The same job done the same way every time: copy a row, send a receipt, move a file. | Jobs that need reading and judgement: summarise, compare, draft, decide what matters. |
| When the input is unusual | It follows the steps anyway, or stops with an error. | It adapts, which is the point and also the risk. |
| What it costs | Usually a set price per run or per task. | What the model reads and writes. It varies from reply to reply, so it needs a cap. |
| How it goes wrong | A step breaks, and nothing happens until someone notices. | It does something plausible but wrong, or keeps going. It needs limits and approvals. |
| Where a person fits | When the flow is built, and when it breaks. | In the conversation: asking, correcting, approving. |
When an automation is the right tool
If you can write the job down as steps that never change, automate it. It will usually be cheaper, faster and more predictable than an agent.
- When an order comes in, add a row to the sheet and send the confirmation.
- Every night, copy new sign-ups into the mailing list.
- When a form is submitted, create the ticket.
Putting a language model in the middle of jobs like these adds cost and a way to be wrong, and gains nothing.
When an agent is the right tool
If the job starts with “read this and work out…”, steps will not cover it.
- Read this week’s orders and tell me what changed and why it matters.
- Compare these three competitors’ pricing pages and draft a paragraph for the client.
- Look at what the team discussed and write the Monday report.
An agent can also be asked a follow-up question, corrected, and told to try again, in the same conversation as everyone else.
Using both: the agent decides, the automation acts
The two fit together well. The automation builder already holds the sign-ins to your apps and knows how to post, send and update. The agent knows what to say and when. Connect them, and the agent can ask the automation to act.
Zapier and Make each offer a tool server for this: you choose the actions an AI agent may use, and they give you a token. In Agora that becomes a connection: a workspace owner pastes the token once, chooses which agents may use it, and decides for each action whether it runs at once or waits for approval.
A sensible split for a social media agent, for example:
- Runs at once: reading last week’s post statistics, looking up what is in stock.
- Waits for a person: publishing a post, sending an email, changing a record. The request shows exactly what will be sent, and nothing happens until someone approves it.
Three mistakes to avoid
- Using an agent for a fixed job. If the steps never change, you are paying a model to follow a recipe.
- Giving an agent a publish button with no approval. An agent can be misled by what it reads. Anything that leaves the building should wait for a yes.
- Leaving agent spending uncapped. An automation has a known price per run. An agent does not, so give it a daily limit in money.
How connections and approvals work in Agora: Product and Safety.