EvidenceGrounded-Agent: An Agentic AI Framework for Traceable Compliance Reasoning over Enterprise Documents

Abstract

As enterprise compliance relies on massive amounts of unstructured regulatory, contractual and policy documents, the requirement for automated reasoning systems that provide verifiable, evidence-based answers, not slick, but unsupported text, continues to grow. In this work, we combine the recent trends of large language models, dense retrieval, agentic reasoning, and explainable AI to propose a conceptual framework, Evidence Grounded-Agent, for compliance reasoning over enterprise document corpora with traceability. The framework is built on the principles of transformer-based language modeling, retrieval-augmented generation, reasoning-actor-loop, self-consistency decoding, and legal NLP benchmarks, which integrates a dense passage retriever, with an iterative planning-reasoning-reflection cycle, and an explicit citation-attribution verification step. Finally, based on the existing literature on hallucination in generative systems, specifically in legal applications, and limitations of post-generation explainability, the synthesis argues for the value of grounding evidence during retrieval time rather than post generation. In a qualitative comparative analysis, the proposed framework is compared with individual language models, retrieval-only pipelines, and reasoning-and-acting agents in five dimensions of capabilities, which shows that retrieval grounding and iterative self-verification fill gaps in traceability and auditability reported in the literature. The historical trajectory, architectural design, and comparative positioning of the framework are summarized in six figures and six tables, and four formalizations describe how to retrieve scores, how to marginalize evidence from generation, how to decode consensus, and a proposed framework of evidence-attribution scores. Three key challenges to enterprise deployment are identified: inference latency, evaluation standardization, and retrieval robustness. The findings suggest that the relation between the traces of compliance and the explanation should be grounded in the architecture and not layered over opaque generation and then validated empirically, thus providing a structured basis for subsequent empirical validation.