Graph Design Cut AI Tool Calls 40% in Code Search

One .NET team cut agent tool calls by 40% for code search—not with better models, but by using graph algorithms to surface exact context. This talk reveals that underneath the GraphRAG hype, foundational graph techniques (schema design, entity resolution) are the real lever for making AI applications cheaper and more reliable.

The key is upfront investment in graph quality: schema-driven extraction from text, ontology rules, and embedding-based entity matching to unify nodes. Without this, queries drown in duplicates. With it, graph traversal effortlessly answers multi-hop questions that cripple SQL, like finding all recipes using garlic across a complex food graph.

Algorithms like personalized PageRank rank relevance in dense citation networks, shortest path reveals code dependency chains that vector search misses, and subgraph matching finds security patterns without knowing symbol names. These are unique, non-trivial capabilities.

But graph construction is not free: it demands data engineering discipline. The 40% number came from a specific .NET codebase; not all domains have graph-shaped problems. Leaders should pilot on dense, relationship-heavy data before committing. The real payoff is reducing costly LLM calls by providing curated, relationship-rich context.