AgentCoord: Visually Exploring Coordination Strategy for LLM-based Multi-Agent Collaboration (2404.11943v1)

Abstract: The potential of automatic task-solving through LLM-based multi-agent collaboration has recently garnered widespread attention from both the research community and industry. While utilizing natural language to coordinate multiple agents presents a promising avenue for democratizing agent technology for general users, designing coordination strategies remains challenging with existing coordination frameworks. This difficulty stems from the inherent ambiguity of natural language for specifying the collaboration process and the significant cognitive effort required to extract crucial information (e.g. agent relationship, task dependency, result correspondence) from a vast amount of text-form content during exploration. In this work, we present a visual exploration framework to facilitate the design of coordination strategies in multi-agent collaboration. We first establish a structured representation for LLM-based multi-agent coordination strategy to regularize the ambiguity of natural language. Based on this structure, we devise a three-stage generation method that leverages LLMs to convert a user's general goal into an executable initial coordination strategy. Users can further intervene at any stage of the generation process, utilizing LLMs and a set of interactions to explore alternative strategies. Whenever a satisfactory strategy is identified, users can commence the collaboration and examine the visually enhanced execution result. We develop AgentCoord, a prototype interactive system, and conduct a formal user study to demonstrate the feasibility and effectiveness of our approach.

• The paper introduces a structured schema and a three-stage generation protocol to design and explore coordination strategies in multi-agent systems.
• The methodology integrates LLM prompting with interactive visualizations, enabling systematic agent assignment and debuggable, iterative workflow refinement.
• Empirical findings show enhanced strategy comprehension, reduced cognitive load, and more efficient exploration compared to traditional text-based approaches.

AgentCoord: Visual Exploration of Coordination Strategies for LLM-based Multi-Agent Collaboration

Introduction

"AgentCoord: Visually Exploring Coordination Strategy for LLM-based Multi-Agent Collaboration" (2404.11943) introduces a visual, structured framework for the design and exploration of coordination strategies in LLM-based multi-agent systems. The motivation stems from limitations in current frameworks, where specifying collaboration through either code-based or natural language paradigms presents accessibility barriers and exacerbates ambiguity and cognitive burden as task and team complexity scale. By addressing these issues through structured representations and interactive visualization, AgentCoord aims to democratize strategy design and enable both novice and expert users to effectively construct, refine, and execute LLM-driven collaborative workflows.

Structured Representation and Three-Stage Generation Method

A key contribution of AgentCoord is the development of a structured schema for multi-agent coordination strategies. Drawing from an analysis of 25 research papers and 7 open-source frameworks, the authors abstract a schema built around a multi-level breakdown:

• Plan Outline: High-level decomposition of user goals into sequential tasks.
• Task: Defined by input/output "key objects" and internal agent collaboration process.
• Key Object: Intermediate artifacts exchanged among tasks and agents.
• Agent: LLM-based entities parameterized by profiles and instructions.
• Action/Instruction: Atomic behaviors assigned to agents, labeled by explicit interaction types (propose, critique, improve, finalize).

This hierarchy allows natural language flexibility to be retained while enforcing structural regularity, directly addressing the problem of ambiguous and cognitively costly text-based coordination specification.

The three-stage generation protocol sequentially produces an executable strategy:

1. Plan Outline Generation: The LLM decomposes user goals into ordered tasks and identifies key objects.
2. Agent Assignment: Candidate agent selection and task-to-agent mapping using agent profiles and LLM assessment.
3. Task Process Generation: Detailed intra-task workflow creation, specifying agent interactions with explicit semantic roles.

Each stage leverages LLM prompting for both initial synthesis and iterative refinement, with opportunities for user intervention at each step.

Visual System and Interactive Exploration

AgentCoord instantiates this schema in an open-source interactive platform with tightly integrated visualization. The interface organizes information into cascading views paralleling the generation stages:

• Plan Outline View: Bipartite graphs link tasks and key objects, supporting structural edits and branching exploration.
• Agent Board View: Agent cards with profiles, current assignments, and heatmap-based visualization of capability-to-task fit, facilitating rapid reassignment and multi-criteria selection.
• Task Process View: Summaries and detailed templates highlight agent roles, input dependencies, and action interaction types using visual encoding.

Crucially, the system offers exploration mechanisms for each design phase:

• Branch-based exploration in plan and task-process stages, supporting rapid generation and comparison of alternative strategies via targeted LLM prompting.
• Agent assignment exploration using LLM-generated “capability scoring,” presented as interactive heatmaps for transparent, multi-dimensional trade-off presentation.

Final execution results are also visually organized, maintaining explicit input-output linkage to the original design, thus mitigating the text overload typical in existing frameworks.

Empirical User Evaluation

A formal user paper with 12 participants, covering a spectrum from LLM system novices to experienced developers, empirically evaluated AgentCoord against two baselines: a text-centric prompt-driven system (AutoAgents) and an LLM "group chat" interface (AutoGen). Quantitative (five-point Likert) and qualitative feedback was solicited on expressiveness, comprehension, exploratory flexibility, result analysis, and overall usability.

Strong Empirical Findings

• Strategy Comprehension: Participants rated AgentCoord as markedly superior due to its consistency and visual clarity. Users noted that visual structure "increases predictability and confidence" relative to unstructured text-based or chat-based coordination.
• Exploration Efficiency: The interactive branching and agent-selection mechanisms led to more systematic and less error-prone exploration, with heatmap-based agent scoring described as "comprehensive and insightful."
• Cognitive Load: Visual linking and adaptive expansion/retraction of information reduced user overwhelm, a commonly cited problem in multithreaded LLM collaborative systems.
• Result Analysis and Correction: Visual traceability from result artifacts back to influencing strategy nodes enabled effective debugging and iteration.

Notably, users expressed a clear overall preference for AgentCoord, with willingness to adopt it for both research and practical workflow prototyping.

Implications and Theoretical Significance

AgentCoord represents a significant shift in interaction design for LLM-agent collaborations, moving from purely symbolic (code/text) to structured, visually mediated co-design. The framework demonstrates that systematic structuring of coordination strategies—mirroring traditional software engineering abstractions, but realized in natural language and LLM-centric paradigms—can align human and LLM reasoning processes. This convergence is reflected in user-perceived confidence, predictability, and ease of strategy refinement.

The integration of LLMs' implicit domain knowledge with transparent, interactive agent selection and process branching mechanisms points toward new directions for human-in-the-loop AI co-design beyond agent orchestration—in simulation, collaborative creativity, and multi-modal task domains.

Limitations and Future Directions

Limitations include the present focus on text-based tasks and static (pre-execution) strategy specification. The authors identify future research opportunities in:

• Generalizing to multi-modal environments with richer key object types.
• Enabling dynamic, in-execution (real-time) strategy adaptation.
• Extending interaction taxonomies and visual encodings for richer social and competitive agent scenarios (e.g., debates, negotiations, complex simulations).
• Incorporating user model adaptation and preference learning for more personalized strategy bootstrapping.

Conclusion

AgentCoord (2404.11943) sets forth a structured, visual paradigm for designing LLM-driven multi-agent collaboration, demonstrating both high empirical utility and a strong theoretical foundation for reducing ambiguity and cognitive overhead in strategy specification. The findings underscore the value of structure-augmented, visually guided, LLM-enabled interfaces for scalable, accessible agent coordination strategy design and highlight a promising trajectory for future AI system human interface research.