Why Your Agent Keeps Forgetting Things (And How to Fix It)

Most agent memory implementations have one thing in common: they don't have one. Here's what a real memory architecture looks like. The Default (Wrong) Approach Nine out of ten agent implementations handle memory the same way: messages = [] # The "memory system" while True : messages . append ({ " role " : " user " , " content " : user_input }) response = llm . complete ( messages ) messages . append ({ " role " : " assistant " , " content " : response }) This works fine — until it doesn't. After 20-30 turns, you hit the context limit. Or you restart the process. Or the user comes back three days later. Gone. All of it. The context window isn't memory. It's working RAM. And you wouldn't run your OS entirely from RAM. The Four Memory Tiers Production agents need four kinds of memory, each with different storage backends, retrieval patterns, and lifetimes: @dataclass class MemoryTier : name : str storage_backend : str max_items : Optional [ int ] ttl_seconds : Optional [ int ] retrieval_