Designing a Modular AI Collaboration System
Multi-Agent AI Workspace | System Design & Interaction Architecture | Shipped 2025
Project overview
Designed a modular AI collaboration system that enables users to create specialized AI agents with individual prompts, data sources, and personalities — all organized under structured domains with controlled access. Focused on simplifying complex AI ecosystems through clarity, hierarchy, and usability.
The project focused on designing a modular AI collaboration system where users can create, manage, and organize multiple AI agents — each with its own behavior, data, and purpose.
The goal was to provide teams with a structured and secure platform for building custom AI ecosystems that mirror real organizational workflows.
Product
Web Application
My Role
Product Designer — System Architecture, UX Design, and Prototyping
Skills
Information Architecture, Interaction Design, System Thinking, Design Systems
Impact
Improved scalability, data security, and collaboration efficiency across multi-agent AI environments
Introduction
Most AI tools today are designed for single-use conversations, making it difficult for teams to scale AI across varied contexts and roles.
This system was conceptualized to address that limitation — enabling users to create modular, domain-specific AI agents that function independently but coexist within a unified workspace.
Problems & project goals
Teams struggled to maintain structure when managing multiple AI use cases, leading to context loss and data overlap.
There was no way to isolate data or control who could access or modify an AI’s behavior.
Goals: build a domain-based hierarchy for access control, design a scalable structure for AI management, and simplify multi-agent interactions through clear visual and interaction design.
Opportunity
This project presented an opportunity to redefine how users interact with multiple AI agents — shifting from single, isolated assistants to a modular, organization-wide AI structure.
By introducing domains and roles, teams could securely collaborate and manage AI behaviors aligned with project goals.
Final designs
The final design introduced a three-level hierarchy: Domains, Agents, and Chats — represented in a three-panel interface for seamless navigation.
Each agent could be customized with a unique system prompt, description, and linked data source, creating flexible yet controlled AI behaviors.
The design emphasized modularity, progressive disclosure, and a clean enterprise visual tone to handle complex structures without overwhelming users.
Outcomes & impact
Users were able to build and manage multiple AI systems within a single platform, improving organization and control.
Domain-level access and data isolation increased trust and data security across collaborative teams.
The structured architecture simplified complex multi-agent workflows, improving clarity and scalability for future integrations.
Takeaways
System thinking is key to scalability — clear hierarchy and structure reduced complexity and improved user confidence.
Designing for control and customization deepened user engagement and adaptability.
Balancing simplicity and capability turned a technically complex system into a clear, approachable experience.