Collabagents Suggestus Collaborationis Multi-Agentium by Jiahui Yu and Xudong Xing

Collabagents Suggestus Collaborationis Multi-Agentium by Jiahui Yu and Xudong Xing is a highly esteemed winner of the very prestigious 2026 Praemium A' Design Award Aeneum in Designatio Interfaciei Competition Category.

AesInterfacies· 2026

Collabagents Suggestus Collaborationis Multi-Agentium

by Jiahui Yu and Xudong Xing

Client: CollabAgents

Praemium A' Design Award Aeneum Winner in Interfacies Category

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Verified Award Winner

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The Design

About Collabagents Suggestus Collaborationis Multi-Agentium

CollabAgents est suggestus collaborationis multi-agentium ad operum cursus implicatos destinatus. Usuarii cursum operis creant et partes hominibus participantibus assignant. Cum pars deest, agens intellegentiae artificialis eam partem suscipit ut processus continuetur. Opus in nodos conexos ordinatur, et unius nodi mutatio gradus sequentes renovat. Communicatio structa, codice fundata, adhibetur ut informatio cum praecisione transferatur et ambiguitas minuatur. CollabAgents cursus operum lineares in systemata accommodabilia convertit quae mutationibus respondent.

The Recognition

About the Praemium A' Design Award Aeneum

Collabagents Suggestus Collaborationis Multi-Agentium victor est Praemii A' Design Award Aeneum, honoris multum aestimati formis egregiis, creandi ingenio, sollertia et acumine insignibus dati. Victores Aenei opera perita et mire creandi sunt, quae experientiam, imaginationem et solertiam auctorum suorum confirmant. Exspectatur Collabagents Suggestus Collaborationis Multi-Agentium rem multum aestimatam, admiratam et carissimam, notabiliter professionalem et novam fore. Haec forma Aeneo victa optimas consuetudines artis, scientiae, formandi rationis et technologiae amplectitur et in cura minutiarum excellit. Collabagents Suggestus Collaborationis Multi-Agentium necessitates egregie explet, cupiditates tangit, sensus capit et curiositatem allicit.

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: CollabAgents is a multi-agent collaboration system that simulates missing roles with AI agents to ensure continuous workflows. Node-level editing enables targeted updates, reducing rework by over 85%, while code-based communication improves precision and speed. It transforms linear workflows into adaptive, resilient systems.
Dimensions & Packaging: The system features a node-based workflow structure, role simulation modules, and dynamic process propagation. It supports real-time updates, modular task execution, and scalable multi-agent coordination. Key capabilities include node-level editing, automatic downstream updates, and role auto-completion to maintain continuous workflow execution.
Tags: multi-agent, collaboration, workflow, AI system, interaction design, role simulation, automation, UX, decision system
Production Technology: CollabAgents is built using AI-driven multi-agent architecture, workflow modeling, and structured data exchange. The system leverages role-based agent simulation, node-level state management, and real-time synchronization to enable adaptive collaboration. Code-based communication ensures precise information transfer and reduces ambiguity across processes.
Design Challenge: The main challenge was designing a system that balances automation with user control in complex workflows.

Traditional collaboration relies heavily on human coordination, making it difficult to handle missing roles, dynamic changes, and process interruptions. The challenge was to create a system that allows targeted intervention without breaking the overall workflow.

This was addressed through node-level editing and role simulation, enabling flexible adjustments while maintaining system stability and continuity.
Research: The project is based on research into cross-functional collaboration challenges, including workflow inefficiencies, role dependency, and communication breakdowns.

Methods included workflow analysis, scenario mapping, and observation of real-world team processes. The research identified key issues such as delays caused by missing roles and the need for more adaptive systems.

The design explores how multi-agent systems can simulate roles, improve coordination, and reduce rework, resulting in more efficient and resilient collaboration.
Inspiration: CollabAgents is inspired by the complexity of cross-functional collaboration, where delays often result from missing roles and fragmented communication. It explores how AI agents can simulate expertise and fill capability gaps in real time, transforming collaboration into a more adaptive and efficient process.
Project Duration: The project was developed between 2025 and 2026 in the United States as an independent concept design focused on future collaboration systems.
Operation Flow: Users start by selecting or creating a workflow and assigning roles. If roles are missing, AI agents automatically step in to continue the process.

Users can interact with specific steps, make changes, and instantly see updates applied to the rest of the workflow. Instead of restarting the entire process, only affected parts are adjusted.

This enables smoother collaboration, faster decision-making, and reduces delays caused by miscommunication or missing inputs.
Patents: No patents or trademarks are registered for this project.
Team Members: Jiahui Yu
Xudong Xing
Image Credits: Image #1: Creators Jiahui Yu and Xudong Xing, CollabAgents, 2025.
Image #2: Creators Jiahui Yu and Xudong Xing, CollabAgents, 2025.
Image #3: Creators Jiahui Yu and Xudong Xing, CollabAgents, 2025.
Image #4: Creators Jiahui Yu and Xudong Xing, CollabAgents, 2025.
Image #5: Creators Jiahui Yu and Xudong Xing, CollabAgents, 2025.