Sprounix AI职业代理 by Xiaoshi Dai, Di Xia and Feifan Gao

Sprounix AI职业代理 by Xiaoshi Dai, Di Xia and Feifan Gao is a highly esteemed winner of the very prestigious 2026 A' Design Award 铁奖 in 数字产品设计 Competition Category.

铁数字产品· 2026

Sprounix AI职业代理

by Xiaoshi Dai, Di Xia and Feifan Gao

Client: Sprounix

A' Design Award 铁奖 Winner in 数字产品 Category

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The Design

About Sprounix AI职业代理

Sprounix是一款由人工智能驱动的界面，旨在通过对话式用户体验简化复杂的求职申请流程。为解决申请疲劳问题，该设计以多模态聊天界面取代了静态表单，通过自然对话捕捉软技能。其用户界面采用流畅的副驾驶仪表盘，提供实时面试辅导和透明的任务追踪。通过整合代理间协议，系统实现了后端协商的自动化，让用户能够通过简化、无压力的交互模式管理高频次申请。

The Recognition

About the A' Design Award 铁奖

Sprounix AI职业代理是 A' Design Award 铁奖得主，这是授予满足人们对设计精良的创作所期待的专业与工业要求的优良设计的声望认可。铁奖得主展现出其创作者的高超技艺、高度专业化、专家级的理解力和创造能力。预计Sprounix AI职业代理将成为一件备受推崇、令人钦佩且被珍爱的创作，既实用又创新。这件赢得 A' Design Award 铁奖的设计整合了行业最佳实践，并拥有与专业知识和才华相结合的称职技术特性。Sprounix AI职业代理因其严谨而受到尊重，旨在提供有助于让世界变得更美好的生活品质改善。

Behind the Design

Press Kit

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

Unique Properties: Sprounix is a chat-centric AI Career Agent that transforms job seeking into a multimodal interactive journey. Beyond static resumes, it uses text and video dialogue to uncover soft skills and aspirations. A key innovation is the Agent2Agent A2A technology, enabling the user’s agent to communicate directly with recruiter systems for hyper-efficient matching. It also provides automated applications and real-time interview coaching, acting as a proactive career advocate.
Dimensions & Packaging: Sprounix is a cloud-based, responsive web platform compatible with all modern browsers. It supports multi-modal inputs including WebRTC video, voice-to-text, and PDF parsing. The matching engine achieves over 85% accuracy by moving beyond keywords to multi-dimensional intent recognition. The system adheres to WCAG 2.1 accessibility and enterprise-grade security standards, ensuring full GDPR compliance for encrypted candidate data protection.
Tags: AI Career Agent, Agent2Agent, Conversational UI, UX Design, Future of Work, Automated Recruitment, NLP, Multi-modal Interaction
Production Technology: Built on a robust AI infrastructure using Large Language Models (LLM) for natural language understanding and multi-modal analysis. The core innovation is the Agent2Agent (A2A) communication protocol that streamlines data exchange between independent AI entities. The system features a responsive React-based front-end for seamless cross-platform use, integrated with real-time video processing and scalable microservices to handle high-frequency job matching and data sync.
Design Challenge: The primary challenge was designing a seamless UX for the complex Agent2Agent backend. We had to ensure users felt in control while their AI agent performed proactive tasks like autonomous applications. We solved this by creating a highly intuitive, chat-first interface that provides transparent "Co-pilot" updates. Integrating multi-modal inputs (text and video) while maintaining a consistent, supportive AI persona across all platforms was a significant technical and design obstacle we overcame.
Research: Our research explored the Job Search Anxiety among early career talents and the inefficiencies of keyword-based recruitment. We used a mixed-methods approach, including more than fifty user interviews and data analysis of traditional ATS systems. The results showed that users provide forty percent more detailed skill data via natural dialogue than static forms. This led to the development of our Chat-centric discovery interface and the Agent2Agent matching protocol, which proved to reduce initial screening time by seventy percent in pilot tests.
Inspiration: The inspiration came from the challenging labor market faced by Gen Z. Traditional job hunting is often repetitive and discouraging. We wanted to create a "Co-pilot" that doesn't just list jobs but actively advocates for the user. By combining AI-driven matching with a friendly, gamified experience, we aimed to lower the anxiety of career-starting and make professional growth feel like a guided, empowered journey.
Project Duration: The project conceptualization started in January 2025 in the United States. Following intensive research and UX prototyping of the conversational engine, the beta version of the platform was developed and finalized in late 2025.
Operation Flow: The experience centers on a conversational interface where users engage with the AI via text or video. This chat-first approach replaces traditional forms, building a deep professional profile. Once the agent understands the candidate's goals, it leverages A2A protocols to negotiate with recruiter agents and automate applications. The co-pilot remains active throughout, providing instant feedback and resume tailoring via a fluid UI.
Team Members: Xiaoshi Dai
Feifan Gao
Di Xia
Image Credits: Image & video design: Xiaoshi Dai, Di Xia, Feifan Gao