AI Agents Series — Most “AI Agents” Aren’t Really Agents - Ep 04

Most “AI Agents” Aren’t Really Agents

A lot of people in AI are talking about “agents” right now. But here’s something I realized while building my own:

👉 A lot of what we call “AI agents” are really just workflows. And that’s okay.

I started by building a simple trend intelligence system:

• Get trends → Generate ideas → Return result.
• Same steps, same order, every time.

🚫 That’s much closer to a workflow than an agent.

A workflow is simple:

👉 You define the steps
👉 The system executes them

Step 1 → Step 2 → Step 3 → Done.

Then I tried to make it feel more agent‑like:

• Let the LLM choose which tool to call
• Pass memory as context
• Use prompts to guide decisions

It felt smarter. But the overall control flow was still something I hard‑coded. I was still deciding:

• what happens next
• how many steps run
• when the system stops

So I made a small change. Instead of fixed steps, I introduced a loop:

👉 Generate → Evaluate → Improve → Repeat

Now the system could refine ideas, retry, adjust. This pushed my workflow toward something more agentic. But even then…

👉 It still needed guidance on when to stop.

That’s when the core difference clicked for me:
👉 A workflow follows a predefined path
👉 An agent discovers the path

In a more agent‑like system:

1. The next step is decided at runtime
2. The system can iterate
3. It evaluates and adjusts
4. It stops when the goal seems achieved, not just when a fixed script ends

The loop becomes: Think → Act → Evaluate → Repeat → Stop.

That evaluation step is what starts to make the system feel agentic—because now it isn’t just executing, it’s deciding whether the result is good enough.

👉 I think, we don’t need “full agents” to build useful systems.

✔️ Structured workflows with LLM‑powered steps will cover most of the tasks.