# CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration

arXiv: https://arxiv.org/abs/2406.13381 · HTML: https://mlsanity.com/paper/2406.13381/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 5 of 5)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [LASER: LLM Agent with State-Space Exploration for Web Navigation](https://mlsanity.com/paper/2309.08172/) (2023)

## Built on by (most cited first, 12 of 12)

- [From System 1 to System 2: A Survey of Reasoning Large Language Models](https://mlsanity.com/paper/2502.17419/) (2025)
- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)
- [Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs](https://mlsanity.com/paper/2502.07942/) (2025)
- [StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management](https://mlsanity.com/paper/2601.05890/) (2026)
- [SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL](https://mlsanity.com/paper/2607.11185/) (2026)
- [Mobile-Agent-RAG: Driving Smart Multi-Agent Coordination with Contextual Knowledge Empowerment for Long-Horizon Mobile Automation](https://mlsanity.com/paper/2511.12254/) (2025)
- [CoDA: A Context-Decoupled Hierarchical Agent with Reinforcement Learning](https://mlsanity.com/paper/2512.12716/) (2025)
- [OS-Marathon: Benchmarking Computer-Use Agents on Vast-Horizon, Repetitive Tasks](https://mlsanity.com/paper/2601.20650/) (2026)
- [AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering](https://mlsanity.com/paper/2605.17352/) (2026)
- [Can LLM Agents Sustain Long-Horizon Organizational Dynamics?](https://mlsanity.com/paper/2606.01199/) (2026)
- [Harness-RL: Black-Box Reinforcement Learning with Action-Args Decoupling for Central-Agent Multi-Agent Harnesses](https://mlsanity.com/paper/2608.29641/) (2026)
- [Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs](https://mlsanity.com/paper/2610.14209/) (2026)

## Abstract (arXiv)

Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks, even equipped with advanced strategies like CoT and ReAct. In this work, we propose the CoAct framework, which transfers the hierarchical planning and collaboration patterns in human society to LLM systems. Specifically, our CoAct framework involves two agents: (1) A global planning agent, to comprehend the problem scope, formulate macro-level plans and provide detailed sub-task descriptions to local execution agents, which serves as the initial rendition of a global plan. (2) A local execution agent, to operate within the multi-tier task execution structure, focusing on detailed execution and implementation of specific tasks within the global plan. Experimental results on the WebArena benchmark show that CoAct can re-arrange the process trajectory when facing failures, and achieves superior performance over baseline methods on long-horizon web tasks. Code is available at https://github.com/xmhou2002/CoAct.