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
Projects where I apply this concept. The projects page shows the code and maturity of each one.
Have a project in mind?
Write to info@gianlucamazza.it with the operational problem and the systems involved. When a working prototype already exists, the first step is the production readiness review. That review does not include code changes.