Energy-policy decisions require evidence that spans scientific, regulatory, institutional, and international sources, yet existing AI-assisted approaches largely automate individual review tasks or generate narrative syntheses. This paper proposes an Agentic Policy Knowledge Synthesis framework for structured policy-answer construction: transforming complex energy-policy questions into explicit, traceable, and evidence-grounded knowledge structures rather than direct narrative answers from large language models (LLMs).
