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About this paper
Anuj Kaul, Qianlong Lan, Pranay Gupta
eBay Inc. researchers and an independent researcher propose AgentBound, a framework that establishes a non-bypassable runtime governance layer for autonomous AI agents, ensuring proposed actions align with human principal intent through a three-authority model and providing cryptographically verifiable accountability via immutable governance receipts.
Key insights
- A "behavioral governance gap" exists where agents can fail to meet principal intent despite valid identity and authorization, requiring a distinct enforcement layer.
- Decoupling governance into independent authorities and composing their judgments via a conservative algebra enables robust and verifiable runtime enforcement.
- Cryptographic receipts offer a new paradigm for agent accountability, moving from implicit trust to explicit, independently verifiable evidence of policy compliance.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.