Structured Knowledge Graphs Cut AI Coding Agent Costs and Hallucinations

Coding agents typically retrieve far more context than a task requires, pulling in loosely related files and code chunks through broad vector searches before writing a single line. This over-fetching imposes a double cost: wasted tokens on unused context and degraded reasoning as relevant information gets buried in noise. The root problem is that standard RAG systems chunk documents arbitrarily and rank results by statistical similarity rather than structural relevance, meaning high similarity scores do not guarantee usefulness. Replacing broad codebase searches with an ontology-based knowledge layer — a machine-readable map of entities, typed relationships, and constraints — allows agents to retrieve only the precise atoms a task requires. Engineers at Betsson's AI-DLC project found this approach simultaneously reduced token spend and improved output quality, since agents traverse defined relationships instead of guessing connections from embedding proximity.