AgentWall: A Runtime Safety Layer for Local AI Agents
The paper introduces AgentWall, a runtime safety layer designed for local AI agents. It addresses the critical issue of safety as these agents evolve into active participants capable of executing commands and modifying files. AgentWall aims to enhance control and oversight by intercepting actions, requiring human approval for sensitive operations, and maintaining an execution trail.
- ▪AgentWall is a runtime safety and observability layer for local AI agents.
- ▪It intercepts proposed agent actions and evaluates them against a declarative policy.
- ▪The system requires human approval for sensitive operations and records a complete execution trail.
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Computer Science > Artificial Intelligence arXiv:2605.16265 (cs) [Submitted on 24 Mar 2026] Title:AgentWall: A Runtime Safety Layer for Local AI Agents Authors:Ashwin Aravind View a PDF of the paper titled AgentWall: A Runtime Safety Layer for Local AI Agents, by Ashwin Aravind View PDF HTML (experimental) Abstract:The safety of autonomous AI agents is increasingly recognized as a critical open problem. As agents transition from passive text generators to active actors capable of executing shell commands, modifying files, calling APIs, and browsing the web, the consequences of unsafe or adversarially manipulated behavior become immediate and tangible.
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