Ask HN: Thinking about memory for AI coding agents

I’ve been experimenting with AI coding agents in real day-to-day work and ran into a recurring problem, I keep repeating the same engineering principles over and over.

Things like validating input, being careful with new dependencies, or respecting certain product constraints. The usual solutions are prompts or rules.

After using both for a while, neither felt right. - Prompts disappear after each task. - Rules only trigger in narrow contexts, often tied to specific files or patterns. - Some principles are personal preferences, not something I want enforced at the project level. - Others aren’t really “rules” at all, but knowledge about product constraints and past tradeoffs.

That led me to experiment with a separate “memory” layer for AI agents. Not chat history, but small, atomic pieces of knowledge: decisions, constraints, and recurring principles that can be retrieved when relevant.

A few things became obvious once I started using it seriously: - vague memory leads to vague behavior - long memory pollutes context - duplicate entries make retrieval worse - many issues only show up when you actually depend on the agent daily

AI was great at executing once the context was right. But deciding what should be remembered, what should be rejected, and when predictability matters more than cleverness still required human judgment.

Curious how others are handling this. Are you relying mostly on prompts, rules, or some form of persistent knowledge when working with AI coding agents?