Google engineers demonstrate how AI systems are evolving from basic retrieval-augmented generation (RAG) to graph-based architectures that handle more complex reasoning and multimodal tasks. The 90-minute workshop walks through building production agent stacks with semantic graph retrieval and specialized agent orchestration.

• RAG is being replaced by graph-based approaches that organize context semantically rather than just retrieving relevant documents
• The production stack involves extracting graph context and orchestrating multiple specialized agents to handle different tasks
• This represents a concrete shift in how companies are building AI systems—from simple retrieval to structured knowledge representation

The article shows how packing specialized, retrieved knowledge into smaller AI agents can match or beat huge frontier models. It explains a structure—raw source extractions, concept entries, theses and a startup primer—plus embedding‐powered retrieval to feed just the right context at query time.

• Structured retrieval (10,000 pages → 381 concept docs + 54 theses via hybrid BM25/semantic search) let smaller models match or beat frontier models on specialized tasks.
• The same pipeline replicated across a dozen domains, from finance to rare medical research to corporate policy.
• A locally-run Qwen model with this "knowledge agent" harness performed comparably to Claude Opus, at zero cloud cost.
• Embedding costs were trivial (under a dollar for thousands of documents using BGE-M3 or OpenAI's text-embedding-3-small) before moving entirely to local hardware.

Karpathy proposes replacing on-the-fly retrieval with an LLM-maintained markdown wiki that ingests sources, compiles structured pages, and self-updates through ingest, query, and lint cycles. This approach builds a persistent, compounding knowledge base without vectors or re-retrieval, though it currently lacks enterprise controls.

• Karpathy's "LLM Wiki" replaces vector-based RAG with an LLM that reads sources once and maintains a persistent, self-updating markdown wiki instead of re-retrieving chunked fragments per query.
• The system runs three operations—ingest (add new material), query (answer from the wiki, then save new answers back into it), and lint (scheduled scans for outdated info, broken links, and gaps)—so every interaction compounds the knowledge base rather than starting fresh.
• It directly targets two RAG failure modes: stateless retrieval that repeats embedding work every query, and chunking that destroys document context.
• The idea echoes Vannevar Bush's 1945 Memex concept of an associative, hyperlinked personal knowledge library, though it currently lacks enterprise-grade controls.