Memory as infrastructure
Memory should be versioned, measured, compressed, and queried through explicit retrieval contracts.
Memory Architectures
Memory layers for AI systems: semantic recall, episodic traces, compression, persistence, and retrieval evaluation.
memory architecture diagram. Nodes: event, encode, episodic, semantic, recall, compress. Flow: event to encode (primary); encode to semantic (primary); semantic to recall (primary); encode to episodic (supporting); episodic to recall (supporting); recall to compress (feedback).
Evaluation
Recall quality needs stable test sets, not anecdotal prompts. The same applies to compression and summarization.
Memory Layer
Episodic memory, semantic retrieval, compression, and replayable context for agents that need continuity.
memory architecture diagram. Nodes: event, encode, episodic, semantic, recall, compress. Flow: event to encode (primary); encode to semantic (primary); semantic to recall (primary); encode to episodic (supporting); episodic to recall (supporting); recall to compress (feedback).
Retrieval Infrastructure
Embedding pipelines, hybrid search, metadata filters, reranking, and stable eval sets for RAG systems.
retrieval architecture diagram. Nodes: docs, chunks, vector, lexical, rerank, eval. Flow: docs to chunks (primary); chunks to vector (primary); vector to rerank (primary); chunks to lexical (supporting); lexical to rerank (supporting); rerank to eval (feedback).
Related projects
Shipped systems where this concept runs in production.
Have a project in mind?
Tell me about the operational problem you want to solve. A few lines about the context and the systems involved are enough for a first assessment.