More agents. The same queue.
In a fictional dispatch library, adding more helpers does not shorten the queue: every independent task waits for the same key. The illustration asks where work must be coordinated and where it can proceed independently.
Original fictional animation with synthetic English narration and English captions. The narration is not Ahmadreza Samadi’s recorded voice. The library, people and helpers are an analogy, not GitHub’s interface or a measured performance test.
Read the English film transcript
Mina runs a fictional dispatch library. Her team just added AI helpers, and they can all prepare work at the same time. Each helper writes its own labelled working version. But to file anything, they all need the same oversized key, so one card goes
in at a time. Add more helpers, and the lock doesn’t open any faster. The queue just grows. In software, a repository stores a project’s versions. A branch, or reference, is a named pointer to one of those versions. Mina’s library is our analogy for
that. GitHub’s engineers describe a similar bottleneck in their Git storage. Writes to a repository were ordered as one, and each write waited for several round trips between distant data centres. GitHub says the shift toward AI agents shaped this work. Its redesign moves the
ordering to individual references, so updates to unrelated references can proceed independently. In Mina’s library, each independent shelf gets its own key. Helpers working on different shelves stop waiting for each other. But two cards for the same shelf still pass one numbered gate, in
order, so nobody sees an older version after a newer one. GitHub presents this as evolving architecture, with internal tests. It isn’t a promise that every team’s agents will run faster. So before you add more agents, map the work. What truly has to happen
in order? What can run on its own? Then measure the whole workflow, not the number of helpers. More helpers only help when the work isn’t waiting on one key. Ahmadreza Samadi. More at a-samadi.com