https://doi.org/10.1051/epjconf/202637001014
Self-Evaluating Agentic Framework Using Retrieval-Augmented Generation for Accountable Environmental Sustainability Communication
Mukesh Patel School of Technology and Management, NMIMS, Computer Engineering Department, 400056 Mumbai, India
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 29 May 2026
Abstract
Effectively communicating information about environmental sustainability is challenging because of complicated policies and multiple stakeholder inquiries. In this paper, we propose a unique agentic framework that brings together meta-classification, retrieval-augmented generation (RAG), and self-evaluating large language models to accomplish that. During the pre-call stage, user inquiries about environmental policy, or what the organization is doing sustainability-wise, are processed through a meta-classifier to either a RAG system, drawing on a knowledge base, or a standard large language model (LLM), with the result surfaced in a dashboard for agents to use. The audio of the calls is then transcribed using Whisper in the post-call stage, and the output processed in a generative evaluation loop where a generator LLM assesses the responses, while another LLM acts as an AI-as-judge to provide feedback about performance. Experimental outcomes have indicated enhanced accuracy, relevance, and trustworthiness, indicating the framework's success in enabling accountable high quality communication of environmental sustainability information.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
