AI Engineering Has Used Graphs Four Ways — The Fourth Could Transform Agent Planning

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A developer and Head of Developer Relations at Dremio argues that AI engineering has successfully applied graphs to knowledge retrieval, corpus-wide RAG, and agent control flow, but has overlooked a critical fourth use: representing the agent's work plan itself as a graph. Currently, when an AI agent executes a task, the internal plan it follows exists only temporarily in the model's context window and is discarded once the session ends. The author contends that making this plan an explicit, inspectable graph structure would allow harnesses to verify steps, check success criteria, and improve reliability. To address this, he developed the Agentic Graph Specification (AGS), which encodes agentic work as a graph of bounded loops with checkable outcomes. AGS is currently running at full conformance in two harnesses he built, called Loro and MagAgent.

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