#Entities

Articles about Entities — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with entities. ← Blog home

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Pratik Dhanave · ·5 min read

Structured and Graph Memory — When Facts Need Shape

Embeddings are a marvel of forgiving representation: throw anything in, get approximate recall out. But some of what an agent must remember isn't approximate — who reports to whom, which service depends on which, what the user decided and when they reversed it. For facts with structure, similarity search is the wrong instrument, and the fix is as old as databases: give the facts a shape. This post is about entity-centric and graph-shaped memory, and when the extra machinery pays.

Similarity search breaks on aggregation, multi-hop, identity, and what-is-true-now. The fix is shape: entity-centric memory with resolved, superseding claims as the 80% answer; knowledge-graph edges (owns, depends_on, supersedes) where relations are the content; and a hybrid end state where the structured layer answers precisely and the episodic store keeps the receipts.

#Agent Memory#Knowledge Graphs#Entities#AI Agents#AI Engineering

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse the blog or explore related topics using the tag cloud above.