Beyond Retrieval: Why Agents Need Memory, Not Just Search

If you're building AI agents right now, you've probably noticed something frustrating. Your agent handles a complex task brilliantly, then five minutes later makes the exact same mistake it just recovered from. It's like working with someone who has no short-term memory.

This isn't a bug in your implementation. It's a design limitation. Most organizations are using Retrieval-Augmented Generation (RAG) to power their agents. RAG works great for what it was designed to do: answer questions by finding relevant documents. But agents don't just answer questions. They take action, encounter obstacles, adapt their approach, and learn from failure. That requires a different kind of intelligence.