SAFE-AGENT: A Safety-Assured Framework for Embodied LLM Autonomy in Large-Scale Retail and E-Commerce Systems

Vasanth Rajendran

Keywords: embodied autonomy, LLM agents, safety assurance, robotic decision systems, risk-bounded control, constraint-aligned generation, retail AI, e-commerce systems, guardrail validation, autonomous decision-making

TL;DR: A robotics-inspired safety framework that treats large-scale retail systems as embodied environments and ensures safe, verifiable LLM autonomy through grounded alignment, risk-aware governance, and multi-stage guardrails.

Abstract:

Large language models (LLMs) are increasingly deployed as autonomous agents in retail and e-commerce systems, where they generate storefronts, construct browse layouts, enrich product attributes, and trigger actions that affect millions of customers and thousands of sellers. Although these environments are digital, these agents function as embodied decision-makers: they navigate structured world models, operate under hard constraints, and induce irreversible state transitions, similar to embodied robotic systems.

This paper presents SAFE-AGENT, a safety-assured autonomy framework that brings robotics safety principles into LLM-driven retail decision systems. SAFE-AGENT integrates three layers: Grounded Decision Alignment, Risk-Aware Action Governance, and Multi-Stage Guardrail Enforcement. A simple risk-bounded validator ensures an action is accepted only when its predicted risk is below a configurable threshold.

SAFE-AGENT provides the first structured blueprint for verifiable and dependable LLM autonomy in global-scale retail systems, showing how methods from safe embodied robotics can ensure practical safety in large and high-impact digital environments.

Submission Number: 20