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AI agents fail in production, and what matters is how you respond in the first twenty minutes. This runbook covers severity classification, isolating before investigating, purging contaminated memory, communicating without inventing facts, and turning every incident into a regression test before closing it as done.
A year after GraphRAG left the lab, one statistic holds: it works where corporate information has dense relational structure, fails where there are only loose documents. Patterns, ingestion costs, and architectural decisions that have survived a year of real deployment.
Knowledge graphs spent two decades waiting for their moment. With LLMs now bridging free text and formal ontology, and the GraphRAG pattern already mature, the technology is back in the spotlight. Time to look at why it finally fits and where it actually pays off.
RAG 2.0 means retrieval built from several sources at once rather than a single vector search: dense embeddings, lexical matching, and knowledge graphs that capture relationships between entities, with a reranking layer ordering the final candidates. The 2023 pattern of one vector database plus an LLM no longer describes what production systems actually do.
The venture capital market in 2024 has partially recovered, but the improvement is uneven. Generative AI absorbs 35-40% of capital while consumer and DTC remain slow. Due diligence timelines have tripled, the burn multiple now dominates investor conversations, and a serious raise takes three to six months from first pitch to close.
