Agents forget everything between sessions — they can never learn or improve

Every time you start a new session with an AI agent, it starts from zero. Two hours of context-setting — explaining your project conventions, debugging approach, preferred tools, past decisions — is gone. So what? You pay the same onboarding cost every single session, forever. An agent that has helped you 100 times is exactly as helpful as one helping you for the first time. It will re-suggest solutions you already tried and rejected. It will re-discover patterns you already established. It will make the same mistakes you corrected yesterday. Why does this matter in the first place? The entire value proposition of a "teammate" vs a "tool" is that teammates learn. A junior developer on day 90 is vastly more useful than on day 1 because they have accumulated context about your codebase, preferences, and past decisions. Agents cannot do this. They are permanently stuck at day 1. This caps their value at "smart stranger who knows nothing about your project" no matter how long you use them. The structural reason: LLM context windows are ephemeral by design. Solutions like RAG over chat history retrieve raw conversation chunks, not distilled knowledge. Vector databases store embeddings but lack the structured reasoning to know when a past lesson applies to a current situation. Nobody has built the knowledge distillation layer that converts session transcripts into reusable agent memory.