The deployment of autonomous AI agents in sensitive domains, such as healthcare, introduces critical risks to safety, security, and privacy. These agents may deviate from user objectives, violate data handling policies, or be compromised by adversarial attacks. Mitigating these dangers necessitates a mechanism to formally guarantee that an agent's actions adhere to predefined safety constraints, a challenge that existing systems do not fully address. We introduce VeriGuard, a novel framework that provides formal safety guarantees for LLM-based agents through a dual-stage architecture designed for robust and verifiable correctness. The initial offline stage involves a comprehensive validation process. It begins by clarifying user intent to establish precise safety specifications. VeriGuard then synthesizes a behavioral policy and subjects it to both testing and formal verification to prove its compliance with these specifications. This iterative process refines the policy until it is deemed correct. Subsequently, the second stage provides online action monitoring, where VeriGuard operates as a runtime monitor to validate each proposed agent action against the pre-verified policy before execution. This separation of the exhaustive offline validation from the lightweight online monitoring allows formal guarantees to be practically applied, providing a robust safeguard that substantially improves the trustworthiness of LLM agents.

The rapid advancement of large language models (LLMs) has catalyzed the deployment of LLM-powered agents across numerous applications, raising new concerns regarding their safety and trustworthiness. Existing methods for enhancing the safety of LLMs are not directly transferable to LLM-powered agents due to their diverse objectives and output modalities. In this paper, we propose GuardAgent, the first LLM agent as a guardrail to other LLM agents. Specifically, GuardAgent oversees a target LLM agent by checking whether its inputs/outputs satisfy a set of given guard requests defined by the users. GuardAgent comprises two steps: 1) creating a task plan by analyzing the provided guard requests, and 2) generating guardrail code based on the task plan and executing the code by calling APIs or using external engines. In both steps, an LLM is utilized as the core reasoning component, supplemented by in-context demonstrations retrieved from a memory module. Such knowledge-enabled reasoning allows GuardAgent to understand various textual guard requests and accurately "translate" them into executable code that provides reliable guardrails. Furthermore, GuardAgent is equipped with an extendable toolbox containing functions and APIs and requires no additional LLM training, which underscores its generalization capabilities and low operational overhead. Additionally, we propose two novel benchmarks: an EICU-AC benchmark for assessing privacy-related access control for healthcare agents and a Mind2Web-SC benchmark for safety evaluation for web agents. We show the effectiveness of GuardAgent on these two benchmarks with 98.7% and 90.0% accuracy in moderating invalid inputs and outputs for the two types of agents, respectively. We also show that GuardAgent is able to define novel functions in adaption to emergent LLM agents and guard requests, which underscores its strong generalization capabilities.