Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2026
Abstract
Environmental, Social, and Governance (ESG) standards have been increasingly adopted by organizations to demonstrate accountability towards ethical, social, and sustainability goals. However, generating ESG reports that align with these standards remains challenging due to unstructured data formats, inconsistent terminology, and complex requirements. Existing ESG lifecycles provide guidance for structuring ESG reports but lack the automation, adaptability, and continuous feedback mechanisms needed to address these challenges. To bridge this gap, we introduce an agentic ESG lifecycle framework that systematically integrates the ESG stages of identification, measurement, reporting, engagement, and improvement. In this framework, multiple AI agents extract ESG information, verify ESG performance, and update ESG reports based on organisational outcomes. By embedding agentic components within the ESG lifecycle, the proposed framework transforms ESG from a static reporting process into a dynamic, accountable, and adaptive system for sustainability governance. We further define the technical requirements and quality attributes needed to support four main ESG tasks, such as report validation, multi-report comparison, report generation, and knowledge-base maintenance, and propose three architectural approaches, namely single-model, singleagent, and multi-agent, for addressing these tasks. The source code and data for the prototype of these approaches are available at https://gitlab.com/for_peer_review-group/esg_assistant.
Keywords
AI agents, ESG standards, LLMs
Discipline
Databases and Information Systems | Software Engineering
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Proceedings of the 23rd IEEE International Conference on Software Architecture Companion, ICSA-C 2026, Amsterdam, Netherlands, June 22-26
First Page
29
Last Page
36
ISBN
9798331557409
Identifier
10.1109/ICSA-C68850.2026.00014
Publisher
IEEE
City or Country
Pistacataway
Citation
HOANG, Thong; KLYMENKO, Mykhailo; XU, Xiwei; PAN, Shidong; DING, Yi; TANG, Xushuo; YANG, Zhengyi; SHI, Jieke; and David LO.
ESG reporting lifecycle management with large language models and AI agents. (2026). Proceedings of the 23rd IEEE International Conference on Software Architecture Companion, ICSA-C 2026, Amsterdam, Netherlands, June 22-26. 29-36.
Available at: https://ink.library.smu.edu.sg/sis_research/11335
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICSA-C68850.2026.00014