CHAP Protocol Aims to Fix Missing Audit Trails in AI Agent Decision-Making

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When AI agents handle consequential tasks like refunds, the human decisions made during review are often not recorded as distinct events, leaving gaps in audit trails. Existing protocols such as MCP and A2A are designed for tool connectivity and agent messaging, not for capturing the reasoning behind human overrides. Researchers Kang and Diponegoro identified this gap by evaluating five agent interoperability protocols against accountability criteria, finding none adequately addressed decision logging. To address this, BrightbeamAI has released CHAP, an open-source protocol under the Apache-2.0 license, which signs and chains decision records to prevent quiet edits and preserve evidence of human interventions. The project is at version 0.2.10 and supports multiple frameworks, with recent updates focused on security and audit hardening informed by external review.

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