Intelligent AI Delegation
Nenad Tomašev, Matija Franklin, Simon Osindero
2026-02-16
Summary
This paper discusses how to make AI agents better at breaking down big tasks and assigning parts of those tasks to other AI agents or even people.
What's the problem?
Currently, AI agents aren't very good at figuring out *how* to split up complex jobs. The methods they use are pretty basic and don't adjust well when things change or when another agent fails to complete their part of the work. It's like giving someone a task without clear instructions or a backup plan if they get stuck.
What's the solution?
The researchers created a new system for AI delegation that's more flexible and thoughtful. It's not just about assigning tasks, but also about clearly defining who's responsible for what, making sure everyone understands the goal, and building trust between the agents involved. This system considers things like transferring authority and accountability, and it works whether the task is being delegated to another AI or a human.
Why it matters?
This research is important because as AI becomes more capable, it will need to work with other AI and with people to achieve really ambitious goals. A reliable system for task delegation is crucial for building these collaborative AI networks, which could eventually become a new kind of internet where AI agents work together seamlessly.
Abstract
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.