What I Learned Adding Memory to AI Agents

I built these systems with heavy use of AI coding tools — prompting, directing, and iterating. The experiments and lessons are real. Most AI agents are goldfish. Every conversation starts from scratch. The agent has no idea what you asked yesterday, what format you prefer your answers in, or that it made the same mistake three sessions ago and you corrected it. For simple Q&A, this is fine. But for agents that are supposed to work with you over time — support agents, analytics assistants, coding copilots — this is a fundamental problem. So I set out to add memory to an agent. How hard could it be? This is the story of what I tried, what broke, and what I actually learned. The naive mental model (and why it falls apart) Before building anything, my mental model was simple: Store useful things the agent learns during conversations When a new conversation starts, retrieve relevant memories Inject them into the prompt Agent is now smarter Steps 1 through 4 are all correct in principle. But