SAFE-AGENT-L: A Legal Compliance and Governance Framework for Autonomous LLM Agents in Large-Scale Retail Systems

Vasanth Rajendran

Keywords: LLM autonomy, legal compliance, AI governance, retail regulation, safety assurance, embodied decision systems, autonomous agents, regulatory AI

Paper Type: Full papers

TL;DR: A governance framework that ensures autonomous LLM agents in retail produce legally compliant, verifiable, and regulation-safe decisions at global scale.

Abstract:

Large-scale retail platforms increasingly rely on autonomous LLM agents to generate product content, classify restricted goods, construct storefronts, enrich attributes, and make merchandising decisions that immediately affect millions of customers and thousands of sellers. Because these systems operate within dense and overlapping legal regimes—including consumer protection law, advertising regulation, product safety mandates, restricted-goods rules, and regional compliance requirements—LLM-generated outputs can trigger real statutory violations. A single hallucinated medical claim, unsafe product classification, or misleading pricing representation can lead to enforcement actions, liability exposure, and systemic marketplace risk.

This paper introduces SAFE-AGENT-L, a legal-compliance–assured governance framework for autonomous LLM agents deployed in high-impact retail environments. The framework integrates three layers: (1) Grounded Legal Alignment, which injects statutory constraints, region-aware rules, and prohibited-claim logic into model reasoning; (2) Risk-Aware Action Governance, which computes a composite risk score using uncertainty measures, violation-prediction classifiers, and legally sensitive feature detectors; and (3) Multi-Stage Compliance Guardrails, which enforce deterministic validation, mandatory overrides, human-in-loop escalation, and safe fallback generation. We formalize a risk-bounded compliance validator and propose evaluation metrics modeled on real regulatory audit procedures, including violation-rate thresholds, safe-output yield, and guardrail efficacy.

By treating retail as a legally regulated embodied environment—where autonomous agents must navigate structured constraints, irreversible state transitions, and jurisdiction-dependent rules—SAFE-AGENT-L provides a principled approach for building trustworthy, auditable, and regulatorily compliant LLM-driven systems. This work aims to bridge the gap between AI autonomy and concrete legal obligations, establishing a blueprint for safe deployment of generative agents in global commerce.

Submission Number: 37