Anchor Routine

Retries seem simple at first, but deciding when an AI agent should retry can be surprisingly difficult.

Too many retries waste resources and can create loops, while too few retries may cause agents to give up on tasks they could have completed successfully.

I'm curious how different teams approach retry strategies in production.

Do you limit retries by task type, confidence score, error category, or something else?

My app opens with a fourteen second welcome film. I wanted a calm ambient score under it and did not want to license a stock track for one screen, so the music is a 50 line numpy script. Warm pad chords built from layered sine waves. Small chime accents placed on the same timestamps as the scene captions. A low hum that slowly breathes underneath. It renders the exact same wav every run and it added about 300KB to the mp4.

Then the browser rules arrive. Autoplay is only allowed muted, so the film starts silent and the score sits behind a Sound on button. The realistic case is that most people watch the whole intro and never find out the audio exists.

I still think it was worth it. The unmuted version feels finished in a way the silent one does not. But I have no number for how many people ever tap unmute. Has anyone measured unmute rates on a product intro video, and were they high enough to justify the audio work?

We put all 111 one click apps on the first screen. It felt like the whole point of the thing, everything in one place, look how much you get.

People opened it, scrolled, and left. Not confused. Just done deciding before they'd made a single one.