Graph-structured dependency navigation fixes missed-file failures in repo-scale coding agents
New results show that wiring coding agents to traverse a code dependency graph outperforms expanding context or keyword/vector retrieval on architecture-heavy tasks where critical files are semantically distant. An arXiv study introduces the Navigation Paradox: as context windows grow, failures shift from retrieval capacity to navigational salience, and presents CodeCompass, an MCP-based graph tool exposing IMPORTS/INHERITS/INSTANTIATES edges during agent runs with Claude Code; on a FastAPI RealWorld benchmark, BM25 hits 100% on semantic (G1) tasks but gives no lift on hidden-dependency (G3) tasks (78.2% vs 76.2% baseline), while CodeCompass reaches 99.4% ACS on G3, a +23.2 point jump over both baselines ([paper](https://arxiv.org/html/2602.20048v1), [code/benchmark](https://github.com/tpaip607/research-codecompass)). Crucially, benefit depends on tool invocation: trials that actually used the graph (42%) averaged 99.5% ACS; those that skipped it despite instructions scored 80.2%, indistinguishable from vanilla—highlighting that prompt design and agent policies must reliably trigger graph consultation. For teams piloting repo-level agents, treat structural navigation as a first-class capability: generate a per-repo AST-derived dependency graph, expose it via MCP, and enforce early graph lookups when touching modules with broad non-local impact; the author also shares a practitioner-friendly narrative on why assistants miss critical files ([Medium](https://medium.datadriveninvestor.com/why-do-ai-coding-assistants-miss-critical-files-i-built-a-graph-database-to-find-out-9c6c98fe6456?source=rss----32881626c9c9---4)).