Towards AI as Colleagues: Multi-Agent System Improves Structured Ideation Processes

TL;DR

This paper presents MultiColleagues, a multi-agent conversational system that enhances collaborative ideation by enabling AI agents to act as colleagues, share ideas, and involve humans in joint problem-solving, leading to higher quality and more novel outcomes.

Contribution

Introduces MultiColleagues, a multi-agent system that facilitates collaborative ideation with humans, demonstrating improved social presence and idea quality over single-agent systems.

Findings

MultiColleagues increased perceived social presence.

Participants rated ideas as more novel and higher quality.

More elaboration during ideation with MultiColleagues.

Abstract

Most AI systems today are designed to manage tasks and execute predefined steps. This makes them effective for process coordination but limited in their ability to engage in joint problem-solving with humans or contribute new ideas. We introduce MultiColleagues, a multi-agent conversational system that shows how AI agents can act as colleagues by conversing with each other, sharing new ideas, and actively involving users in collaborative ideation processes. In a within-subjects study with 20 participants, we compared MultiColleagues to a single-agent baseline. Results show that MultiColleagues fostered stronger perceived social presence, and participants rated their outcomes as higher in quality and novelty, with more elaboration during ideation. These findings demonstrate the potential of AI agents to move beyond process partners toward colleagues that share intent, strengthen group dynamics, and collaborate with humans to advance ideas.