Recently been exploring a really efficient agent workflow pattern—combining self-learning with human validation loops.

Here's how it flows:

1. Agent hits something new in its process
2. Stops and asks you to confirm before locking it in
3. Approved learnings get stored in a vector database, then retrieved via hybrid search on the next execution cycle

It's elegant because you're not drowning in auto-save chaos. The human stays in the loop at the right moments, and the retrieval layer actually remembers context across runs. Memory infrastructure doesn't need to be complicated—sometimes the simplest architecture wins.