AI Summary: The paper introduces a formal framework for AI-to-AI and human-to-AI delegation that prioritizes trust and accountability. ' The authors argue that current multi-agent systems are too brittle because they lack dynamic feedback loops.
As advanced AI agents evolve beyond query-response models, their utility is increasingly defined by how effectively they can decompose complex objectives and delegate sub-tasks. We propose an adaptive framework for intelligent AI delegation that moves beyond simple heuristics to incorporate the transfer of authority, responsibility, and accountability. This framework establishes clear specifications regarding roles and boundaries, clarity of intent, and mechanisms for establishing trust within complex delegation networks. Grounded in historical insights from human organizations and key agentic safety requirements, our approach aims to inform the development of robust protocols for the emerging agentic web. The proposed system is applicable to both human and AI delegators, ensuring verifiable task execution and scalable distribution in high-stakes environments.
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