Large language model agents in watershed environmental response: a hypothesis-and-theory framework for the cognitive orchestration layer

Introduction Watershed environmental governance increasingly combines dense sensing infrastructure with institutions whose attention, authority, and coordination capacity remain limited. We develop the cognitive orchestration layer as a theory-building concept for examining how large language model (LLM)-based agents may connect sensing, assessment, decision advice, institutional response, and feedback without displacing domain models or public authority. Methods The Agentic Framework for Watershed Governance (AFWG) specifies three enabling pathways, three institutional interfaces, and four propositions. A purposively selected set of eight publicly documented cases is used to illustrate functional configurations, source limitations, and research gaps; it is not a systematic review or empirical evaluation. Results The cases document sensing, assessment, and decision-support functions, but do not establish improved decision quality, sustained institutional use, environmental outcomes, or agent-executed response closure. MAESTRO is retained as an adjacent-domain design illustration, not an existence proof of full-loop closure: human authorization remains necessary, and its reported performance has not been independently replicated. Discussion Transfer to watershed governance is plausible only where domain models remain authoritative, outputs carry provenance and uncertainty, named officials retain authority to authorize consequential actions, and institutional audit and failure handling are built into deployment. AFWG therefore offers a bounded conceptual framework and a falsifiable research agenda, rather than a claim that agents have already closed the watershed response loop.