Collabagents Ko'p Agentli Hamkorlik Platformasi by Jiahui Yu and Xudong Xing

Collabagents Ko'p Agentli Hamkorlik Platformasi by Jiahui Yu and Xudong Xing is a highly esteemed winner of the very prestigious 2026 A' Design Award Bronza Mukofoti in Interfeys, Interaktiv va Foydalanuvchi Tajribasi Dizayni Competition Category.

BronzaInterfeys· 2026

Collabagents Ko'p Agentli Hamkorlik Platformasi

by Jiahui Yu and Xudong Xing

Client: CollabAgents

A' Design Award Bronza Mukofoti Winner in Interfeys Category

9,696

Verified Award Winner

Verify Award Status for #183243

View at A' Design Award Press Release Award Badge

Colors

The Design

About Collabagents Ko'p Agentli Hamkorlik Platformasi

CollabAgents — murakkab ish jarayonlari uchun mo'ljallangan ko'p agentli hamkorlik platformasidir. Foydalanuvchilar ish jarayonini yaratadilar va unda ishtirok etuvchi shaxslarga rollarni tayinlaydilar. Biror rol bo'sh qolganda, sun'iy intellekt agenti ushbu rolni o'z zimmasiga oladi va jarayon davom etadi. Ish o'zaro bog'langan tugunlar (nodlar) shaklida tashkil etiladi va bitta tugunni tahrirlash undan keyingi bosqichlarni yangilaydi. Ma'lumotlarni aniqlik bilan uzatish va noaniqlikni kamaytirish uchun tuzilmali, kod asosidagi kommunikatsiyadan foydalaniladi. CollabAgents chiziqli ish jarayonlarini o'zgarishlarga javob beradigan moslashuvchan tizimlarga aylantiradi.

The Recognition

About the A' Design Award Bronza Mukofoti

Collabagents Ko'p Agentli Hamkorlik Platformasi A' Design Award Bronza Mukofotining g‘olibi bo‘lib, bu ijodkorlik, zukkolik va topqirlik bilan ajralib turuvchi a’lo dizaynlarga beriladigan o‘ta nufuzli e’tirofdir. Bronza g‘oliblari o‘z dizaynerlarining keng tajribasi, tasavvuri va topqirligini tasdiqlovchi juda mohir va hayratlanarli darajada ijodiy buyumlardir. Collabagents Ko'p Agentli Hamkorlik Platformasi yuqori qadrlanadigan, hayrat bilan qaraladigan va suyukli, diqqatga sazovor darajada kasbiy va innovatsion buyum bo‘lishi kutiladi. A' Design Award Bronza Mukofotini qo‘lga kiritgan ushbu dizayn san’at, fan, dizayn va texnologiyadagi eng yaxshi amaliyotlarni o‘zida mujassam etadi va tafsilotlarga e’tibori bilan a’lodir. Collabagents Ko'p Agentli Hamkorlik Platformasi ehtiyojlarni go‘zal qondiradi, istaklarga tegadi, tuyg‘ularni maftun etadi va qiziqishni jalb qiladi.

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.