New approach to agent reliability, AgentSpec, forces agents to follow rules
1 min read
Summary
Researchers at Singapore Management University have developed a new approach to ensure the reliability of large language model-based AI agents, which are often used in enterprise settings and for self-driving applications.
Existing approaches, such as ToolEmu, effectively identify risks but are susceptible to adversarial manipulation because they are lacking in interpretability and have no mechanism for safety enforcement.
AgentSpec works as a runtime enforcement layer, intercepting the behaviour of the agent and ensuring it behaves only within the parameters desired by the user.
Tests have shown AgentSpec to prevent 90% of unsafe code executions and to ensure compliance in autonomous driving law-violation scenarios, eliminating hazardous actions in embodied agent tasks with millisecond-level overhead.
The approach could become increasingly important if ambient agents, which continuously run in the background and trigger themselves to execute actions, are the future of agentic AI.