Learning to Assign Prediction Tasks to Agents with Capacity Constraints

Researchers propose a machine learning framework for optimally assigning prediction tasks to heterogeneous agents (humans or AI systems) subject to capacity constraints. The work develops explore-exploit algorithms that learn agent expertise and adapt assignments dynamically, demonstrating improvements over baseline approaches across tabular, image, and text tasks.

This research addresses a practical challenge in distributed AI systems: how to intelligently route tasks when multiple agents have different expertise levels and operational limits. The problem mirrors real-world scenarios in enterprise AI, where organizations deploy mixed teams of AI models and human experts with varying capabilities and availability. The theoretical framework characterizes task assignment through three dimensions—agent capacities, expertise variance, and task context—providing principled foundations for what has traditionally been a heuristic problem.

The work builds on multi-armed bandit theory and contextual bandits, extending these frameworks to handle constraints that reflect realistic operational conditions. Sequential learning is critical because agent performance often depends on task characteristics that aren't known in advance. The experimental validation across diverse modalities (tabular data, images, text) and agent types (LLMs, humans) strengthens confidence that the approach generalizes beyond narrow use cases.

For organizations deploying AI systems at scale, this has immediate relevance. Rather than static task routing rules, dynamic assignment policies could reduce costs by directing tasks to optimal agents while respecting resource limitations. This proves particularly valuable when combining expensive human expertise with cheaper AI labor. The inclusion of LLMs and humans suggests the framework can handle heterogeneous agent types with dramatically different costs and capabilities, enabling more efficient hybrid workflows.

Looking forward, extensions might address multi-agent dependencies, temporal dynamics in agent expertise, and online learning with feedback delays. Practical implementation would require integrating real-time performance metrics and cost functions into operational systems.

• →Sequential learning algorithms optimize task-to-agent assignment while respecting capacity constraints and heterogeneous expertise.
• →The framework combines theoretical characterization with explore-exploit strategies showing systematic improvements over non-contextual baselines.
• →Experimental results span multiple prediction tasks including tabular data, images, and text with both AI and human agents.
• →The approach enables more efficient hybrid workflows by dynamically routing tasks to optimal agents based on learned expertise.
• →The contextual-bandit foundation suggests the methodology generalizes across diverse agent types including large language models.