MUSTER Framework Limits AI Agent Data Access and Redundant Actions in Enterprise Use

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A developer has built an experimental AI framework called MUSTER to address two key risks in enterprise AI agents: excessive access to private data and unnecessary repeated actions when outcomes are uncertain. Rather than granting a central AI agent broad access to all systems, MUSTER keeps each data source isolated, with separate agents for payroll and site-access data operating within their own boundaries, enforced via Google Cloud IAM. Facts are only accepted as authoritative when validated and cryptographically signed by the originating source, while a language model's interpretation alone carries no decision-making authority. The framework also introduces a principle of minimal evidence collection, where the agent stops gathering data once no remaining uncertainty can change the final outcome. In a worked example, MUSTER resolved a payroll dispute for a worker named Ravi by confirming his on-site duration exceeded the policy threshold, issuing a corrected payment without needing to establish an exact figure.

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