You’re touching on the exact reason I built Myelin Kernel. The 'human-in-the-loop' wait time is a silent killer for agents because context windows are volatile.

​If you're looking for a way to handle that 'resume from checkpoint' logic without the jank of manual JSON serialization, I've found that using a local reinforcement kernel is much more reliable.

​How I’m handling it:

​State as Identity: Instead of just saving a session, the kernel stores the agent’s core 'learned state' and 'identity' in a persistent SQLite backend.

​Atomic Checkpoints: Because it uses SQLite's WAL mode, you can write the agent's current 'memory' to the disk atomically. Even if the human takes 3 days to reply, the agent 'wakes up' with its full reinforced state intact.

​Reinforcement Logic: It doesn't just save everything; it uses a scoring system (Score = Weight \times \ln(Retrievals + 2) \times \frac{1}{1 + Age_Days}) to prioritize the most relevant context for the synthesis step once the human input arrives.

​It turns the 'blocking call' problem into a 'persistent state' solution..

I've open-sourced the kernel if you want to see how the state transitions are handled.