智能AI委托

摘要

人工智能体已能处理日益复杂的任务。为实现更宏大的目标，智能体需要具备将问题有效分解为可管理子模块的能力，并能安全地将这些子任务委托给其他AI体或人类协同完成。然而，现有的任务分解与委托方法仍依赖简单启发式规则，无法动态适应环境变化，也缺乏对意外故障的稳健处理能力。本文提出一种自适应智能委托框架——通过包含任务分配决策序列，同时整合权限转移、责任归属、权责界定、角色边界明确定义、意图清晰传达以及多方信任建立机制。该框架适用于复杂委托网络中的人类与AI委托方/受托方，旨在为新兴智能体网络中的协议开发提供理论支撑。

English

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.

智能AI委托

Intelligent AI Delegation

摘要

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