The Agent Memory Problem Nobody Solves: A Practical Three-Layer Architecture for Persistent Context
Every production agent eventually collides with the same fundamental problem: the session is stateless, but the user's context isn't. Prior conversations, domain rules, and user preferences need to survive across runs — and most teams end up duct-taping Redis and a vector database together without any principled model for what should go where. A practical post on DEV Community proposes a three-layer architecture that makes these distinctions explicit: working memory (in-context, current session only), episodic memory (past sessions retrieved via vector search), and semantic memory (structured facts, user profiles, domain rules that don't change per session).
The core of the architecture is a hierarchical `recall()` function that checks working memory first, falls back to episodic vector search when relevance drops below a threshold, and pulls from semantic memory for structured facts. The post provides concrete TypeScript implementation patterns for each layer — when to evict from working memory to prevent session bloat, how to structure vector embeddings for episodic storage, and how to keep semantic memory consistent when agents modify their own knowledge store. A failure modes section covers the three most common production breakdowns: bloated sessions from over-snapshotting, stale episodic recalls surfacing outdated context, and semantic memory drift under agent edits.
The three-layer model is simple enough to implement in an afternoon but covers the practical needs of most production agent deployments. More importantly, it gives teams a principled vocabulary for reasoning about what kind of memory a given piece of information should live in — a distinction that determines both retrieval cost and how that information ages over time.