Behavioral Governance for Autonomous AI Agents: The AgentBound Framework
Autonomous AI agents increasingly perform consequential actions on behalf of human principals, including financial transactions, external communications, and enterprise workflows. Existing agent infrastructure relies on identity federation and delegated authorization to authenticate workloads and control resource access, but it cannot determine whether an authorized action should be executed under the current behavioral and operational context. We present AgentBound, a runtime governance framework that provides verifiable behavioral oversight for autonomous AI agents. AgentBound evaluates each proposed action using three independent authorities: delegated authorization, owner-signed behav...
| Authors | Anuj Kaul, Qianlong Lan, Pranay Gupta |
|---|---|
| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 1 July 2026 |
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