A Formalized Method for Pre-Domain Validation of a Multi-Agent Runtime with a Prospective Application to Power Supply
DOI:
https://doi.org/10.33042/2079-424X.2026.65.3.01Keywords:
Multi-Agent System, Artificial Intelligence, Power Supply, Technical Operation, Runtime Environment, Action Validation, DispatchingAbstract
We present a formalized method for pre-domain validation of an LLM-based multi-agent runtime and assess its feasibility through experiments. In the model, an agent proposes an action, and a central controller validates it while maintaining state, the event source, and the journal. Nine adaptive engineering trials were conducted on the Dice Ritual testbed with five persistent players under 13- and 15-category rulesets. The primary endpoint is automatic completion of every round with correct original final scorecards, bonuses, totals, and ranking; automatic player and controller corrections are allowed. Seven attempts produced conditionally verified correct final results, one completed with an incorrect final result, and one stopped before completion. Review of the seven correct results covered 35 scorecards and 475 categories. In one such game, an unrepaired intermediate display error did not change the score of the action actually selected; in another game, a category error caused an unwarranted bonus and an incorrect final total. Original strict verdicts and separate post-game adjudications are retained. The results support the possibility of correct automatic execution under tested conditions, without guaranteeing success in every run. Numerical findings depend on explicit reconstruction assumptions; the exact content of some delivered packets is unavailable. K5 is an auxiliary representation of dice values whose causal benefit was not evaluated. Physical correctness and operational benefit for an actual power system remain outside this assessment.
References
1. Binyamin, S. S., & Ben Slama, S. (2022). Multi-agent systems for resource allocation and scheduling in a smart grid. Sensors, 22(21), 8099. https://doi.org/10.3390/s22218099
2. Costa, G. B., Damiani, J. S., Marchesan, G., Morais, A. P., Bretas, A. S., & Cardoso, G., Jr. (2022). A multi-agent approach to distribution system fault section estimation in smart grid environment. Electric Power Systems Research, 204, 107658. https://doi.org/10.1016/j.epsr.2021.107658
3. Debenedetti, E., Zhang, J., Balunović, M., Beurer-Kellner, L., Fischer, M., & Tramèr, F. (2024). AgentDojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents. Advances in Neural Information Processing Systems, 37, 82895–82920. https://doi.org/10.52202/079017-2636
4. Doshi, A., Hong, Y., Xu, C., Kang, E., Kapravelos, A., & Kästner, C. (2026). Towards verifiably safe tool use for LLM agents. In Proceedings of the IEEE/ACM 48th International Conference on Software Engineering (ICSE-NIER ’26) (pp. 201–205). https://doi.org/10.1145/3786582.3786839
5. Gai, O., Novoseltsev, O., Vorushylo, A., Khomyak, O., & Gai, H. (2024). Features of multi-agent evaluation of of microgrid systems efficiency in parallel operation with the power system according to the reliability criterion. Machinery & Energetics, 15(3), 73–83. https://doi.org/10.31548/machinery/3.2024.73
6. Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Wang, J., … Schmidhuber, J. (2024). MetaGPT: Meta programming for a multi-agent collaborative framework. International Conference on Learning Representations. https://openreview.net/forum?id=VtmBAGCN7o
7. Huang, X., Ruan, W., Huang, W., Jin, G., Dong, Y., Wu, C., … Mustafa, M. A. (2024). A survey of safety and trustworthiness of large language models through the lens of verification and validation. Artificial Intelligence Review, 57(7), 175. https://doi.org/10.1007/s10462-024-10824-0
8. International Electrotechnical Commission. (2013). IEC 62443-3-3:2013 Industrial communication networks—Network and system security—Part 3-3: System security requirements and security levels. Geneva, Switzerland: Author.
9. International Organization for Standardization. (2024). ISO 55000:2024 Asset management—Vocabulary, overview and principles. Geneva, Switzerland: Author.
10. International Organization for Standardization & International Electrotechnical Commission. (2023). ISO/IEC 42001:2023 Information technology—Artificial intelligence—Management system. Geneva, Switzerland: Authors.
11. Izmirlioglu, Y., Pham, L., Son, T. C., & Pontelli, E. (2024). A survey of multi-agent systems for smartgrids. Energies, 17(15), 3620. https://doi.org/10.3390/en17153620
12. Li, G., Hammoud, H. A. A. K., Itani, H., Khizbullin, D., & Ghanem, B. (2023). CAMEL: Communicative agents for “mind” exploration of large language model society. Advances in Neural Information Processing Systems, 36, 51991–52008. https://doi.org/10.52202/075280-2264
13. Li, X., Wang, S., Zeng, S., Wu, Y., & Yang, Y. (2024). A survey on LLM-based multi-agent systems: Workflow, infrastructure, and challenges. Vicinagearth, 1(1), 9. https://doi.org/10.1007/s44336-024-00009-2
14. Liu, X., Yu, H., Zhang, H., Xu, Y., Lei, X., Lai, H., … Tang, J. (2024). AgentBench: Evaluating LLMs as agents. International Conference on Learning Representations. https://openreview.net/forum?id=zAdUB0aCTQ
15. Olorunfemi, B. O., & Nwulu, N. (2023). Multi-agent system implementation in demand response: A literature review and bibliometric evaluation. AIMS Energy, 11(6), 1179–1210. https://doi.org/10.3934/energy.2023054
16. Pérez Hernández, M., Puchkova, A., & Parlikad, A. K. (2024). Multi-agent learning of asset maintenance plans through localised subnetworks. Engineering Applications of Artificial Intelligence, 127, 107362. https://doi.org/10.1016/j.engappai.2023.107362
17. Rokhforoz, P., Montazeri, M., & Fink, O. (2023). Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units. Reliability Engineering & System Safety, 232, 109081. https://doi.org/10.1016/j.ress.2022.109081
18. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed., Global ed.). Harlow, England: Pearson.
19. Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). Gaithersburg, MD: National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
20. Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., … Wen, J. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6), 186345. https://doi.org/10.1007/s11704-024-40231-1
21. Wooldridge, M. (2009). An introduction to multiagent systems (2nd ed.). Chichester, England: Wiley.
22. Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., … Wang, C. (2024). AutoGen: Enabling next-gen LLM applications via multi-agent conversations. Conference on Language Modeling (COLM 2024). https://openreview.net/forum?id=WpkVmCo8d0
23. Xiao, Z., Liu, H., Zhou, X., Cong, S., & Ju, Y. (2026). Power ops agent: A knowledge and data dual-driven multi-agent framework for substation operation and maintenance. Expert Systems with Applications, 318, 131965. https://doi.org/10.1016/j.eswa.2026.131965
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Lighting Engineering & Power Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The fact of the author's acceptance of the terms and conditions hereof is that the author sends his/her materials for publication in the Licensee's journals.
BY THIS AGREEMENT, THE AUTHOR SHOWS THAT THE SUBMITTED MATERIAL:
- does not infringe the copyrights of other persons or organizations;
- has not been previously published in other journals and has not been submitted for publication in other journals.
THE RIGHTS THAT THE AUTHOR TRANSFERS TO THE EDITORIAL BOARD OF LIGHTING ENGINEERING & POWER ENGINEERING, INCLUDE, IN PARTICULAR, THE RIGHTS:
- to publish the article in English and distribute its printed version;
- to distribute the electronic version of the article by any electronic means (placement on the official website of the journal, in electronic databases, repositories, etc.).
The author reserves all rights under the terms of the Creative Commons Attribution License International CC-BY.
In all cases, the presence of a bibliographic link to an article or a hyperlink to its electronic copy on the official website of the journal is mandatory.
Public Agreement On the Procedure for Publishing Author’s Articles
@LepeJournal