The Multiplayer AI Manifesto

AI has gone from answering questions to taking actions on your systems.

An AI agent running on an employee’s machine might inadvertently move sensitive data from one system it’s connected to into another — or worse, onto the public Internet.

When employees of a large enterprise were allowed to collaborate both with each other and with AI, they achieved the highest proportion of top-quality outcomes in a Harvard Business School study.

And companies like Shopify found that allowing all employees to chat with AI agents in public was a boon to productivity. Within a month, 1 in 8 pull requests was already co-authored by their AI agent River.

No, ChatGPT and Claude team/enterprise plans only let you share a few settings: prompts, skills, and integrations. They do not allow true multiplayer in the sense that Slack, Google Docs, Figma, and other modern tools do.

There is no chatting with a teammate’s agent. There is no learning from each other. Context is trapped in individual sessions, unindexed and short-lived.

The simplest solution that allows your team to work with agents together is adding them to Slack. And indeed, Claude Tag and other solutions are great starts.

But a lot of work happens off Slack: in emails, Google Docs, GitHub, Salesforce, ServiceNow, etc. Truly multiplayer AI must enable reaching the same agent session from every work surface and every device.

• Agents must live next to the work, instead of in their own chat or in Slack.
• Every person who is involved in the work must be able to chat directly with the same agent.
• Agents must be able to use every tool that a human on the team uses to do the work.

• A human must never be asked a question that an agent already has the answer to.
• Chasing project updates and relaying answers is work for an agent, not a human.
• Humans must focus only on the highest value activities. The bureaucracy must be digitized.

• When agent sessions live in the cloud, you can pick up every project exactly where you left off, with no context lost.
• Every artifact, whether a design doc, financial plan, or pull request, must have an agent session that can be resumed even months later.
• A new person joining a multiplayer AI team is productive on day 1.

• An agent tricked by a malicious email or document can send sensitive data to an outsider. Security people call this prompt injection.
• Every agent must be limited to the services you approve. That is only practical in locked-down cloud environments.

• Agents are at their most useful when they have access to sensitive data such as emails, Slack messages, financial records, etc.
• When adding another teammate to a session that has exposure to sensitive data, it is important to handle permissions correctly.
• For example, the agent should only have access to integrations that every member of a shared session has access to.

• Some teammates prefer Claude, some prefer ChatGPT, and your CFO prefers open source.
• The most powerful model changes week by week, and most tasks do not require the most powerful model in the first place.
• When you bet your company on one model provider, you align yourself with their incentive structure, which is to bill you for as much usage as possible.

• Governance means being able to answer four questions about any session a year later: which agent ran, who asked it to, what data it touched, and what it changed. Regulated industries and anyone subject to GDPR cannot skip this.
• Auditors need a “black box” for agents, just as they have for every system of record: enough to check what happened without redoing the work by hand.