The three-layer AI stack: Memory, Search, Reasoning

A clear architectural pattern is emerging for AI-native products. The Memory layer handles who the user is — fact extraction, knowledge graphs, temporal handling, conflict resolution, and user profiles (Supermemory’s approach). The Search layer finds the right information — BM25 for labels, vectors for meaning, hybrid for both, reranking for quality (Hybrid search is the default, not the exception). The Reasoning layer navigates complex information — PageIndex for structured documents, Agentic search beats RAG for live codebases for code, multi-hop for following trails.

The infrastructure insight: all three layers could run on PostgreSQL scales further than you think — pgvector for semantic search, pg_textsearch for BM25, regular tables for memory and profiles. This is Context is the product, not the model made architectural — the model is a commodity, but the Memory + Search + Reasoning stack you build around it determines product quality. It’s also the foundation for what Persistent agent memory preserves institutional knowledge that walks out the door with employees describes at the Memory layer: persistent, structured recall that outlives individual sessions. In practice, the Memory layer uses Tiered retrieval prevents context overload — summaries first, details on demand — pulling summaries first, drilling to details only when needed — to stay within the token budgets that make this architecture viable.