Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs

2026-05-07

AI Summary

This work addresses the imbalance between structure and flexibility in existing multi-agent collaboration frameworks powered by large language models, which often leads to inefficiency and resource waste. To overcome this limitation, the authors propose LATTE—a novel framework inspired by distributed systems—that introduces a dynamically evolving, shared task graph to explicitly encode subtask dependencies, agent assignments, and progress states. By integrating LLM-based multi-agent systems with distributed coordination protocols, LATTE enables adaptive collaboration structures, dynamic task allocation, and emergent task discovery. Empirical results demonstrate that LATTE significantly reduces token consumption, execution time, and communication overhead across diverse collaborative tasks, while minimizing file conflicts and redundant outputs. Moreover, it achieves accuracy on par with or superior to strong baselines such as MetaGPT.

Abstract

Large language models (LLMs) are increasingly deployed in teams, yet existing coordination approaches often occupy two extremes. Highly structured methods rely on fixed roles, pipelines, or task decompositions assigned a priori. In contrast, fully unstructured teams enable adaptability and exploration but suffer from inefficiencies such as error propagation, inter-agent conflicts, and wasted resources (measured in time, tokens, or file operations). We introduce Language Agent Teams for Task Evolution (LATTE), a framework for coordinating LLM teams inspired by distributed systems, where processors must operate under partial observability and communication constraints. In LATTE, a team of agents collaboratively construct and maintain a shared, evolving coordination graph which encodes sub-task dependencies, individual agent assignment, and the current state of sub-task progress. This protocol maintains consistency while empowering agents to dynamically allocate work, adapt coordination, and discover new tasks. Across multiple collaborative tasks and a variety of base models, we demonstrate how LATTE reduces token usage, wall-clock time, communication, and coordination failures (e.g. file conflicts and redundant outputs) while matching or exceeding the accuracy of standard designs including MetaGPT, decentralized teams, top-down Leader-Worker hierarchies, and static decompositions.

Problem

• language agent teams
• coordination efficiency
• task decomposition
• resource waste
• inter-agent conflicts

Innovation

• adaptive task graphs
• language agent teams
• coordination framework
• dynamic task allocation
• LLM collaboration