It looked useful. It wasn’t. That’s how we knew the platform was working.
A few weeks ago, we designed and tested an agent internally that seemed like a great idea. It passed the smell test, ticked a few value boxes, and even had a good name.
But when we started using it, something was off.
It didn’t break. It didn’t hallucinate.
It just wasn’t good enough.
The Agent: Contextual Rewrite Assistant
The agent’s goal was to rewrite short blocks of content — social posts, email intros, or section headers — using a selected voice, goal, or tone.
Simple premise. Lots of potential use cases.
It was designed to:
- Take a short input
- Apply a defined voice (e.g. persuasive, informative, relaxed)
- Adjust for platform or goal (e.g. Twitter, email, conversion)
- Output a clean, usable version in a few seconds
We even chained it to an optional style primer to enhance consistency.
So far, so good.
Why It Failed in Use
The first few runs seemed promising. Then we tried to use it across actual workflows. That’s when the cracks showed.
1. The prompt was too brittle
If the input was even slightly unclear, the output missed the mark. There wasn’t enough built-in intelligence to account for nuance, sarcasm, or layered meaning.
2. The output felt generic
Even with tone applied, it lacked identity. Most rewrites felt like something anyone could generate using ChatGPT with a decent prompt.
3. There was no “aha”
You want an agent to deliver something that feels surprising, efficient, or better than what you could’ve done manually. This one didn’t.
It wasn’t wrong. It just wasn’t useful.
What We Learned
Instead of tweaking endlessly, we shut it down and wrote up what we’d learned. Here’s what stood out:
- Agents need edges. A broad tool with no clear opinion tends to become bland. Niche beats vague.
- There’s a difference between capability and usefulness. Just because an agent can perform a task doesn’t mean it should.
- Speed doesn’t matter if the output still needs work. If a user has to edit it every time, the value erodes fast.
- Built-in defaults matter. We didn’t add strong enough fallback logic for poor inputs. That killed trust.
And maybe most importantly:
- You shouldn’t be afraid to kill your own ideas. The platform made it easy to test, realise the problem, and move on.
Why This Strengthens the Platform
A failed agent isn't a failure of the system. It’s a sign the system is working.
We could test, learn, refine, and remove it in hours — not weeks.
That same loop is now built into how we support creators and partners.
It means:
- Better agent design
- Faster iteration
- More room to try things without long-term risk
You don’t need to get it perfect on the first try. You just need a platform that lets you learn fast.
Final Thought: Some Ideas Are Better as Lessons
This agent won’t be in the launch catalogue. But the next five that are will be stronger because of it.
In a space full of prototypes with no feedback loop, we’re aiming for something different.
Real users. Real signals. Real outcomes.
Even when the answer is no.
Want to build something that might fail — and improve because of it?
Join the community and test with us. We’re launching in September, and the best ideas often start with small stumbles.
