Your Multi-Agent System Has a Memory Problem

Every multi-agent system I've seen break in production broke for the same reason: agents couldn't remember what already happened. Not hallucination. Not bad prompts. Memory. The system had no way to persist decisions, track what was active, or share context between agents that needed to coordinate. I run 39 agents in a production operating system. Solving the memory problem was harder than designing the agents themselves. The three kinds of agent memory Agent memory isn't one thing. It's at least three distinct problems, and most systems only solve the first. 1. Session memory (what happened in this conversation) This is what you get for free with most LLM APIs. The context window. It works until the conversation ends, then it's gone. # This is NOT memory. This is a conversation buffer. messages = [ { " role " : " user " , " content " : " Deploy the new config " }, { " role " : " assistant " , " content " : " Config deployed to staging. " } ] # Session ends. Everything above disappears