AGrail introduces an adaptive guardrail system that protects LLM agents from both task-specific and systemic security risks in real-world applications.

• Employs a continuous safety detection approach that evolves with the agent's operations
• Combines explicit rules with LLM-based reasoning for more comprehensive protection
• Features adaptability to new security threats without requiring retraining
• Demonstrates superior performance compared to existing guardrail systems

This research addresses critical security concerns for organizations deploying AI agents, providing a framework that maintains safety while preserving the agent's problem-solving capabilities in dynamic environments.

AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection