Designing Guardrails and Safety Mechanisms for Autonomous Enterprise AI Agents Preventing Hallucinations, Runaway Actions, and Data Leakage

Author(s)	Sandeep Nutakki
Country	United States
Abstract	The deployment of autonomous AI agents in enterprise environments introduces novel safety challenges beyond those encountered in traditional software systems. Unlike deterministic programs, LLM-powered agents exhibit stochastic behavior, may hallucinate incorrect information, can take unintended actions with real-world consequences, and risk exposing sensitive data. This paper presents a comprehensive safety framework for enterprise AI agents, addressing four critical risk categories: hallucination detection and mitigation, runaway action prevention, data leakage protection, and human-in-the-loop escalation. We describe architectural patterns including output validators, action sandboxes, PII detection pipelines, and budget-based circuit breakers. Evaluation across production scenarios demonstrates 91% precision in hallucination detection, 96% F1-score in PII identification, and a 2.3% false positive rate for safety interventions. Our framework enables organizations to deploy autonomous agents while maintaining appropriate risk controls and audit capabilities required for enterprise compliance.
Keywords	AI Safety, Guardrails, Hallucination Detection, Data Privacy, Autonomous Agents, Enterprise AI, Risk Mitigation
Field	Engineering
Published In	Volume 7, Issue 3, March 2026
Published On	2026-03-25
DOI	https://doi.org/10.70528/IJLRP.v7.i3.2015
Short DOI	https://doi.org/hbtqjz

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