ChatGPT Agent = Big Flop?

DEEP DIVE
ChatGPT Agent = Big Flop??

ChatGPT Agent has been out for a couple of weeks now.

And this was supposed to be a glimpse of AGI?

Well I have some thoughts then…

I actually planned to write a deep dive on “10 use cases for ChatGPT Agent.”

But honestly, I couldn’t even come up with more than 3. That says a lot.

The launch felt promising. Like a real step forward. A ChatGPT that actually does things, not just talks about them.

I mean the demo makes it look like something that will make a big difference. It gave me Manus AI vibes.

So I went ahead and gave it a real task.

Nothing crazy. I needed transparent PNG logos for a list of AI tools I was featuring in a YouTube video. The kind of thing that takes me 5–7 minutes usually, just enough to be annoying.

I gave ChatGPT Agent the list and asked it to find the transparent PNGs version.

Here’s the chat

Finding these logos is a bit of tedious work, you Google “Canva logo transparent png” and then pray.

And to its credit… it worked. Slowly. But it worked.

It took 17 minutes… And if I needed 100 logos, this might’ve saved me some time.

But it didn’t feel like a breakthrough.

There was no “wow” moment. Just a shrug. And based on how quickly the hype vanished after launch, I don’t think I was alone.

So what’s missing here?

There’s a tweet I really liked recently from Balaji:

That’s exactly my feeling and ChatGPT Agent is fitting in that category.

The demo promised something magical. A full loop. You say something, it gets done.

But what you actually get is:

1. A slightly smarter way to start something.

2. And a slightly messier way to end it.

We’re still stuck in the in-between. And that makes the experience feel… mid.

Now, prompting has become a lot less important over the past 6 months. Models are becoming much more efficient and if you have a lot of convo with your LLM, that means context, that means less prompting engineering needed.

It’s less of:

“You are a helpful assistant, here is all the context, now please…”

and more: “Do this…”

That part’s moving in the right direction.

But verification is still a real “bottleneck”.

To give you an example, I’ve been building with tools like Lovable, Replit, Cursor, and the biggest pain point is always the same:

1. You make a change, it half-works, then breaks something else.

2. You fix that, and something new breaks.

Even when you explicitly ask the AI to check for errors, to test its output, to validate before continuing, it skips, it assumes, it just kind of wings it and to be honest, gets lazy.

(Maybe to save on compute and inference cost?)

So you end up doing a lot of the work anyway.

Which is fine if you’re technical. But it defeats the whole purpose if you’re not.

Where this might be going

I do think a lot of this will change once AI agents are fully integrated into your browser.

When your workspace becomes the context window.

When you don’t have to explain what you’re doing, because the agent can already see it, that’s when prompt engineering really dies.

That’s when the “just do this” instruction actually lands.

And it also means more reliable results, because your agent will know more than what’s in the current message. It’ll know your project. Your tools. Your patterns.

In the meantime, we’re left in the middle.

And for now, you still need a “specialist/expert” that know what they’re doing and can check on the AI output.

Not quite automation.

Not quite delegation.

Just an eager assistant you still have to babysit.

As the article from Balaji mentions:

0% AI is slow

100% AI is slop.