Electric grid operators are overwhelmed with the data needed to manage complex grid operations, including weather, state-space estimation, network congestion, bidirectional electricity flow, DER coordination, and asset failures. We developed an agentic grid orchestration framework that enables operators to utilise GPT-5 with multimodal data inputs within a power-flow solver, incorporating fail-safe contingencies. We built five agents - each with their own objective function, such as asset health monitoring, reliability, safety, power purchase cost reduction, etc., to operate an optimal system under physical constraints. We struggled to fine-tune the agents, so actions defaulted to conservative choices, and we were unable to connect the refined front end to our backend fully.
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