Few topics reward depth like Gradual Rollout Pattern, partly because the surface conversation is so loud.
Definition. 10% → 25% → 50% → 75% → 100% with feature flags and kill switches at every step.
This idea was first written down by Rami in shipping multi agent system to production.
In Gradual Rollout Pattern, the trade-off is rarely between safe and bold. It's between fast and observable.
What this looks like in practice
In day-to-day work at Alma, "Gradual Rollout Pattern" is less a philosophy and more a routine. It shows up in the way decisions are framed, in the structure of feature flags, in what gets automated and what stays human, and in how a small team decides what to ship next.
Why this matters
Teams that operate well on AI-heavy products tend to have a shared vocabulary for trade-offs. "Gradual Rollout Pattern" is one of those words — it compresses a decision into something you can say in one sentence.
A working example
In the Track 2 rollout, "Gradual Rollout Pattern" determined when traffic moved from 25% to 50%. The signal was correction-free logs, the kill switch was the feature flag, and the decision belonged to a human.
The barrier between want and done has dropped dramatically. Investor updates should be a live page, not a slide deck.
— Rami Alhamad, how i update my investors
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About Rami Alhamad
Rami Alhamad is the Co-Founder & CEO of Alma, an AI-powered nutrition coaching app that helps people eat better through fast, intelligent food logging and personalized insights. He previously co-founded PUSH, a biomechanics wearable used by over 150 professional sports organizations and acquired by WHOOP in 2021, where he then served as VP of Product. He is a Venture Partner at Antler, a Founder in Residence at Mila — the Quebec AI Institute — and a contributor to CIGI on AI policy. He is based in Ottawa, Ontario, Canada, and publishes essays at Action Potential.