The next evolution beyond a single agent like Autoresearch is a platform for agent swarms to collaborate on a single codebase. AgentHub is conceptualized as a "GitHub for agents," designed for a sprawling, multi-directional development process.

Anthropic's new "Agent Teams" feature moves beyond the single-agent paradigm by enabling users to deploy multiple AIs that work in parallel, share findings, and challenge each other. This represents a new way of working with AI, focusing on the orchestration and coordination of AI teams rather than just prompting a single model.

Early AI adoption by PMs is often a 'single-player' activity. The next step is a 'multiplayer' experience where the entire team operates from a shared AI knowledge base, which breaks down silos by automatically signaling dependencies and overlapping work.

Most AI tools are single-player experiences. Linear is designing its agent sessions to be shared, collaborative spaces. Multiple people, like a PM and a designer, can jump into the same chat with an agent, see its work, and give it feedback together, collapsing the collaboration loop.

Using AI agents in shared Slack channels transforms coding from a solo activity into a collaborative one. Multiple team members can observe the agent's work, provide corrective feedback in the same thread, and collectively guide the task to completion, fostering shared knowledge.

Current AI hits like ChatGPT are "single-player" tools. The next breakthrough will be a social, multiplayer experience built on network effects and user-generated content, an area where technically-focused AI labs are culturally weak.

The next frontier for AI isn't just personal assistants but "teammates" that understand an entire team's dynamics, projects, and shared data. This shifts the focus from single-user interactions to collaborative intelligence by building a knowledge graph connecting people and their work.

Asana's CEO argues its key differentiator is a "multiplayer mode" where entire human teams can collaboratively train and correct an AI agent within a project. This contrasts with typical one-on-one chat interactions, creating a unique, compounding learning environment for the agent that Asana believes cannot be easily replicated.

The next wave of AI agents is moving beyond individual use ('single-player mode') to exist within shared team spaces like Slack channels. Tools like Claude Tag embed agents with full team context, transforming them from personal tools into collaborative resources that better mirror how organizational work actually happens.