AGNTCon + MCPCon Japan 2026

Graph-based code intelligence (not GraphRAG) is the infrastructure primitive that AI coding agents need to reason about large codebases without burning tokens. In this session, we'll evaluate various approaches and identify where the field is converging.

Agents fall back to grep because retrieval solutions return a probabilistic approximation of the code at a given point in time. The context goes stale on changes and loses fidelity when going from code to embeddings. By exposing a call graph, based on established compiler theory, to the LLM through a series of functions and tools, the LLM doesn't need to waste tokens because it has access to deterministic, structured, quick access data from which it can get cheap, reliable answers.

Sourcegraph, Meta's Glean, Google's Kythe, GitHub's Stack Graphs, and newer efforts like GitNexus and Aleutian Trace are all building graph based offerings for LLMs. This session provides a deep dive into the problems faced by agents working in large codebases, maps the solution space of graph based coding tools vs more common LLM solutions— RAG, GraphRAG, pure traversal, embeddings — and then shows where each wins or breaks.