01
Narrow jobs
Each agent owns one signal and one kind of output. A small job is easier to check, easier to trust and easier to switch off.
How we build · RemuApp case study
Six narrow agents. One rule: a person approves every change.
RemuApp already collects the signals that show where it should go next: what writers ask for, where they get stuck, how consistent the scores are, and how the service is running. The team reviews each one directly. The next stage is designed to scale that, with small AI agents that turn signals into proposals while people keep the final say.
A person approves every change
Next stage · in design
The loop: signal → proposal with evidence → a person approves → build → tests & evals → release → measure the result → new signals. Nothing ships without approval, and the scoring itself stays with the people responsible for it.
Tap any circle to read about that agent.
Design principles
02
Evidence first
Every proposal arrives with the data behind it, so the person reviewing it can judge the reasoning, not just the conclusion.
03
People decide
Agents propose. People approve, and anything that touches how a manuscript is scored stays in human hands.
What's next for RemuApp.
RemuApp is live today at remuapp.com. The agents shown here are the design the team is building toward.
Product direction by Steven Champion, founder of Remu Technologies.