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Paper · arXiv

Agentao: A Policy-Governed Runtime Harness for Embeddable Tool-Using LLM Agents

LLM agents increasingly operate as execution systems that invoke tools, modify local state, use persistent memory, and interact with external protocols. These capabilities make agents useful, but they also introduce risks related to over-privileged actions, weak auditability, prompt injection, tool poisoning, and uncontrolled side effects. This paper presents Agentao, a governed local-first runtime for tool-using LLM agents. Agentao separates model-generated action proposals from host-authorized execution through a layered architecture consisting of host-facing surfaces, a host contract, a runtime core, a permission-mediated tool system, and supporting subsystems for memory, replay, plugi...

AuthorsBo Jin, Qiang Jiao, Xin Tong
Primary categorycs.AI
Published4 July 2026
Last updated at source28 August 2026
cs.AIcs.MA
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