Flashy demos don’t create retention. Useful tools do.
Right now, AI is everywhere - but useful AI? That’s still rare.
A lot of what’s being built today is impressive in theory and forgettable in practice. Not because the models aren’t capable, but because the products aren’t designed to solve anything real.
It’s easy to ship a shiny feature.
It’s harder to build something people use twice.
At Anjin, we’re trying to stay on the right side of that divide. Here’s how.
The Trap: Building AI for AI’s Sake
You know the one.
Someone spins up a model wrapper, calls it an agent, adds a prompt selector and a dropdown, and calls it a day. It looks good in a deck. It works once in a demo. And then? It drifts into irrelevance.
The core issue: no one stopped to ask what problem it was solving.
We’ve seen this across the industry: AI introduced to tick a box, not serve a need.
When the solution drives the process - not the problem - users feel it immediately.
And they bounce.
Discovery First, Then Design
Before we build anything new into Anjin, we try to answer one question:
“Where’s the actual pain?”
That means real discovery:
- Talking to users, not just surveying them
- Observing workflows, not just output
- Understanding hesitation, confusion, friction
Only then can we shape agents that respond to something meaningful.
We’ve killed off promising ideas simply because we couldn’t map them to a real need.
We’re fine with that. Because what gets used beats what gets built every time.
From Tool to Habit
A great agent is one you trust to handle something boring, valuable, or time-sensitive - again and again.
But that trust doesn’t come from novelty. It comes from:
- Predictable outputs
- Clear boundaries
- Fast, reliable execution
- Honest error handling (or better: helpful recovery)
The goal isn’t to wow users once.
The goal is to make the agent part of their rhythm.
And to get there, you have to resist the temptation to overcomplicate.
What We’ve Learnt the Hard Way
A few lessons from our own experiments:
- If the user doesn’t know when to use it, they won’t.
- If the agent does too much, it usually does too little well.
- If the output needs human correction every time, it’s not helpful.
- If you can’t explain the point of the agent in one sentence, it’s probably not ready.
Some of our best agents came from shrinking their scope - not expanding it.
Hype is Easy. Habit is Earned.
The truth is: novelty wears off. Quickly.
If you want to build an AI product that lasts, start with boring questions:
- “What’s the tedious task no one wants to do manually anymore?”
- “Where are decisions getting bottlenecked by lack of context?”
- “What’s the first thing someone opens when they start work - and how can we live there?”
AI isn’t about replacing humans. It’s about earning a slot in their day.
Final Thought: Make Something People Come Back To
We don’t need more AI that impresses in a live demo.
We need AI that integrates into real behaviour. That fits the flow. That earns repeat attention by actually doing its job - quietly, reliably, and with just the right amount of opinion.
That’s what we’re trying to build at Anjin.
Not hype.
Habit.
Want to build agents that get used, not just shipped?
Join the community or catch up on more posts from behind the scenes:
- Inside Anjin #10: Agents We Haven’t Built (Yet)
- Inside Anjin #09: Designing for Permission, Not Just Access
- Inside Anjin #08: Measuring What Matters
