AI agent vs automation - how to use them

If you're involved in AI automation, you've probably come across terms such as "AI agent" or "agentic systems". They sound like science fiction, but they are more real than ever.

However, the term "agent" is often used misleadingly in the context of automation. In this article, we take a down-to-earth look at what really lies behind the concept and how it differs from traditional automation.

Automation with AI: efficient, but rule-based

Tools such as Make, IFTT or Zapier have greatly simplified the way we complete recurring tasks. You build a workflow: "If X happens, then do Y."

If you like, you can now also add AI models such as ChatGPT. For example, to analyze a message or rate a text. Sounds like AI magic? But it's still classic process automation.

Even if an AI module is involved, the process remains rule-based for the time being. You have to define every if-then scenario yourself beforehand. The automation works intelligently, but does not make its own decisions.

What makes real agent systems special

Agent systems are more than just "smart workflows". They consist of three central components:

1. A language model (e.g. GPT) - the brain, so to speak.
2. A memory - so that the agent can remember previous information.
3. Tools - such as calendar, drive, e-mail or databases that the agent can use independently.

The key difference: an agent does not need specific instructions for each step. Instead, you give it a goal - and it decides independently how to achieve this goal.

Practical example: Agents with N8N

The open source tool N8N allows exactly this approach. You can build a setup there in which the Agent:

• knows what tools he has,
• thinking with a language model,
• memorizes things,
• and flexibly decides what makes sense and when.

One example:

"Please see what appointments I have tomorrow. Book me a Zoom meeting in a free slot and then upload the briefing to Google Drive."

In classic automation, you would have to define each step individually. An agent in N8N, on the other hand, makes its own decisions:

• how it queries the calendar,
• how it recognizes free slots,
• which tool he uses and when,
• and how he documents the process.

When you should use what

The simple rule is:

• For clear, recurring processes: classic automation is completely sufficient.
• For flexible, context-dependent processes: prefer to rely on agent systems.

So if you just want to confirm an email or trigger a Slack post - go with Make. But if you need an AI that can act flexibly depending on the content, context and goal, agents are the better choice.

Conclusion: Make the right decision - and stay realistic

Not all automation has to be an agent. But if you are aware of the differences, you can make better decisions - for yourself, your project or your company.

Agents are not hype, but an exciting development. And tools like N8N make it easier than ever to get started. Just give it a try - and remember:

Just because it has AI in it doesn't make it an agent.

Good luck with automation - or building agents!