AI agents are able to tackle increasingly complex tasks. To achieve more ambitious goals, AI agents need to be able to meaningfully decompose problems into manageable sub-components, and safely delegate their completion across to other AI agents and humans alike. Yet, existing task decomposition and delegation methods rely on simple heuristics, and are not able to dynamically adapt to environmental changes and robustly handle unexpected failures. Here we propose an adaptive framework for intelligent AI delegation - a sequence of decisions involving task allocation, that also incorporates transfer of authority, responsibility, accountability, clear specifications regarding roles and boundaries, clarity of intent, and mechanisms for establishing trust between the two (or more) parties. The proposed framework is applicable to both human and AI delegators and delegatees in complex delegation networks, aiming to inform the development of protocols in the emerging agentic web.

翻译：AI代理能够处理日益复杂的任务。为实现更宏大的目标，AI代理需要能够有意义地将问题分解为可管理的子组件，并安全地将这些子任务的完成委托给其他AI代理及人类。然而，现有的任务分解与委托方法依赖于简单的启发式规则，无法动态适应环境变化，也无法稳健地处理意外故障。本文提出了一种用于智能AI委托的自适应框架——该框架涉及任务分配的一系列决策，同时整合了权限转移、责任划分、问责机制、角色与边界的明确规范、意图清晰度以及建立双方（或多方）间信任的机制。所提出的框架适用于复杂委托网络中的人类与AI委托方及受托方，旨在为新兴智能体网络中的协议开发提供参考。