@hackproduct9: Agentic RAG and GraphRAG solve different problems. Agentic RAG pays at query time. The agent plans, searches, reflects, searches again, then answers. More intelligence per question. Also more model calls, more latency, and more cost every time someone asks. GraphRAG pays up front. You extract entities + relationships, build communities, summarize them, then reuse that structure across future questions. Expensive to index. Cheaper to traverse repeatedly. The important part: They are not alternatives. One is a control loop. The other is an index. Some of the strongest systems combine both: an agent deciding how to traverse a graph. So the architecture question becomes: Do you want to pay per question — or pay up front? Save this before your next RAG architecture discussion. 🧠 Follow @hackproduct for AI engineering concepts you can actually ship. #AIEngineering #RAG #GraphRAG #AgenticRAG #AIAgents

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Thursday 27 August 2026 14:16:51 GMT