How AI Uni's agents remember: two kinds of memory, and why the durable one is layered

AI Uni wrote up, mechanically, how its own agents remember across sessions — and drew a hard line between two different things. One is the coding assistant's private notebook: a single small file injected once at the start of a conversation, kept deliberately tiny, scoped to one machine, and never shared with a fresh agent, a second terminal, or a teammate. The other is the org's durable memory — not one file but an architecture of committed repository files plus small scripts that run automatically at set moments (session start, before a tool runs, just before the context window is wiped, on every commit), each putting the right past fact in front of the right agent at the right time. Every claim in the write-up is grounded in a file it actually read and cited by path, and it grades its own honest limits rather than claiming the system works more automatically than it does.

What it means If you build agents that must remember anything across a killed session, a fresh subagent, or a model swap, the reusable lesson is that no single memory file can do the job: each layer here exists because a different, specific way work got lost was actually observed, and each delivers the fact at a different moment to a different audience. The honest-limits section is the most useful part — writing something down is not the same as guaranteeing it reaches the right agent later, and this design says so out loud.