It is argued the loop is indivisible, tune where and how tightly it may close, state eight structural failure modes as falsifiable predictions, and specify six governance modules that rebuild the authorization frame until it covers the loop, before the loop starts turning.
Abstract
Energy system operation contains a loop of work that automation has never taken over: posing the optimization problem the current cycle should solve, disposing of infeasibility, sequencing a solution into interlocked switching orders, assembling evidence no single model holds, negotiating adjustable capacity with many parties, and settling experience into practice. Licensed dispatchers carry all of it in person, and the rising share of variable renewable generation is making that loop turn faster than their number can grow. Agentic AI supplies the abilities it requires, but enters as the outer loop of control: it calls SCED and the other decision models rather than being called by them. We propose A-CPES, three nested rings, an authorization and accountability frame around an agentic control outer loop around a six-layer CPES core. We argue the loop is indivisible, tune where and how tightly it may close, state eight structural failure modes as falsifiable predictions, and specify six governance modules that rebuild the authorization frame until it covers the loop, before the loop starts turning.
A solver-grounded design principle is presented: a numerical result is reported only when it originates from a trusted tool and passes explicit verification, and a four-group evaluation framework spanning task utility, solver-grounded correctness, faithfulness and safe failure, and cost and latency is proposed.
Daniel Rojas, Abdulwahab Albassam, Aidan G. Leung et al.· arXiv.org· 0 citations
It is argued that agentic AI governance is four problems, not one, each with a mature governing science, and a five-level maturity model with a non-compensatory bottleneck-weighted index and assessment instrument operationalizes CASE as a scientific rather than process maturity model, grounded in production enterprise...
Srinivas Telukunta, Georgios Nektarios Lilis, Lucio Baron· 0 citations
A controlled, physics-grounded benchmark built around planning-induced control trajectories: the ordered planning operations and directives through which an execution architecture acts on other agents and the physical process is introduced.
These results establish a fail-closed workflow for agentic MIP and MILP energy scheduling under the declared physical model and establish a fail-closed workflow for agentic MIP and MILP energy scheduling under the declared physical model.
The proposed framework for implementing an AI decision-maker and automation requires an IT specialist to implement it properly, and the AI model’s accuracy depends on the amount of input data.
SolarChain-Eval is a physics-constrained benchmark for evaluating trustworthy economic agents and formulates market governance as a Gymnasium-compatible Markov Decision Process, where agents make hourly decisions.