Three Memory Mistakes That Kill AI Agents in Production

Last week I watched an agent spiral into a loop, repeatedly trying to complete a task it had already finished three runs ago. The fix took five minutes once I understood what went wrong. The problem? It had no memory of its own history. Here are the three memory mistakes I see most often in autonomous agent code, and how to fix each one. Mistake 1: Treating the context window as your only memory The context window feels like memory because the model can "see" everything in it. But it's not memory — it's working space. When your process exits, it's gone. When you hit the token limit, the oldest parts are truncated or dropped. I've seen agents built where the entire history lives in the conversation list: messages = [] def run_agent (): while True : response = client . messages . create ( messages = messages , ... ) messages . append ({ " role " : " assistant " , " content " : response . content }) # This list grows forever, context gets truncated, agent "forgets" old context Two problem