This post is part of the akm-knowledge series. Part ten introduced the improve pipeline — what each phase does and how to schedule it. This post goes deeper on what continuous operation looks like in practice: the hardware numbers, the reliability bugs we hit at 48 runs per day, and the observability layer we built to keep watch.
Most people think of AI agent memory as something that happens during a session. You talk to your agent, it learns things, maybe you save a few notes, the session ends. The next session starts cold.
akm improve is built around a different model: a continuous background process that runs on your own hardware, against local models, and quietly curates your agent's knowledge base while you work on other things. No cloud API required. No per-token billing for the maintenance pass. A GPU you already own, a model you already have downloaded, running on a schedule.
This post covers what 24 hours of autonomous operation actually looks like, how consumer-grade GPUs handle the load, the reliability work that makes continuous operation viable, and the observability layer that lets you know it's working without watching logs.
What akm improve Does in 24 Hours
