Memory for AI Agents: Understanding Modeling for Unstructured Data

Adrian Brudaru's article delves into the intricacies of modeling unstructured data for AI agents, illustrating how traditional structured data concepts can be applied to this domain. It introduces the Canonical Data Model (CDM) as a vendor-agnostic ontology that helps achieve data consistency and decoupling in structured data environments, while dimensional models optimize query speed. The semantic layer has gained importance as it aids large language models (LLMs) by providing a governed context, preventing them from producing inaccurate queries. For unstructured data, knowledge graphs serve as a canonical model, organizing data as nodes and edges to maintain meaning and relationships. The piece highlights Cognee, a Python SDK and knowledge engine, which builds structured memory for AI agents through ingestion, graph construction, graph maintenance, and hybrid retrieval. This system turns scattered data into a single, structured memory system, improving query performance and providing a robust framework for context engineering, which is increasingly adopted by enterprises like Bayer.