Agent memory and state patterns for loops

This episode is part of my loop engineering series, and it's about the piece that decides whether a loop actually gets better over time: memory.

I talk about what memory actually is, since it gets confused with two other things constantly: the context window, which disappears the moment a session ends, and a project knowledge file, which stays static across runs. Memory is neither. It's the dynamic record of what a loop decided, what failed, and what's still open.

From there I walk through three memory patterns, starting with the flat text file, through task trackers, to structured stores for when semantic search actually earns its complexity. I cover how to wire memory updates into hooks so they happen automatically, and four specific ways memory setups break in practice. Hope you find it useful.

Show Notes

Full article: Substack link

Related episodes:

• Loop Engineering: Putting agents on Autopilot

• The missing guide for AI Agent Skills – Part I

• The missing guide for AI Agent Skills – Part II

• Coming soon: Parallel agents — worktrees, sub-agents, and Agent Teams