What changes when you add an agent to GraphRAG? Here's a quick Q&A 🕸️ Agentic GraphRAG: How does an AI agent use a knowledge graph? ❓ How is it different from regular GraphRAG? 💡 The agent gets control of graph retrieval. At runtime it decides where to start, which tool to call, how far to follow relationships, and when it has enough context. ❓ How does it work in practice? 💡 A ReAct loop: check the schema, run a query, observe the result, repeat. "Which other customers hit the same issue?" takes three steps: ・Vector search finds the matching ticket ・A query finds the error the ticket reports ・A query reaches other customers who reported that error ❓ Is it safe to let it write to the graph? 💡 Only if reads and writes are separated. Writes are guarded by narrow permissions, schema checks, confidence thresholds, or human approval. A context graph that stores past decisions and outcomes extends the same idea. ❓ When is it worth building? 💡 When retrieval paths vary by question, the domain keeps changing, or past decisions should inform future ones. Fixed paths are often faster, so start with a working GraphRAG and add autonomy only where it falls short. #GraphRAG# #AIAgents#