Design and Implementation of a RAG-Based AI Agent for ESG Compliance

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Key Points

The research aims to design and implement an AI system that interprets complex ESG regulatory documents efficiently.
Developed a cloud-native architecture using Google Cloud Platform (GCP) for the AI agent.
Utilized Document AI for recognizing layout and structure of regulatory documents.
Implemented a multilingual embedding model to vectorize structured document chunks for semantic search.
The system achieved high context precision, recall, faithfulness, and answer relevancy in evaluations using the RAGAS performance module.
Generated responses based on retrieved documents and metadata filtering via a LangChain retriever.
Validated the quality of evidence-based responses through a reproducible automatic evaluation framework.

Study implements an AI agent for ESG regulation interpretation, suggesting improved compliance response capabilities.

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Shin et al. (2026) studied this question.

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