Why AI agents need a shared workspace, not another chat window
Most AI tools pair one person with one assistant. Real work involves teams. Here's why agents belong inside a shared workspace with persistent context.
Resources
How teams and agents actually work together
Writing, guides, and reference material on collaborative AI work. This library grows as RelayRealm does.
Agent concepts
Start with the ideas
Multiplayer AI collaboration
Why one-person-one-agent sessions break down as soon as more than one person cares about the outcome.
Shared context
What changes when workspaces, projects, and conversations persist instead of resetting every session.
Agent roles and identity
How giving an agent a role, scope, and history makes its work reviewable by a team.
Permission models for agents
Granting specific capabilities rather than broad access, and where human approval belongs.
Blog
Articles
Longer pieces on multi-agent collaboration, AI-native development, and building for teams where agents participate.
Giving AI agents roles and permissions: a practical model
Broad access makes agents risky and hard to review. A role-based model — clear scope, specific tools, human approval points — makes agent work trustworthy.
Multi-agent collaboration, explained
What it means for several specialized AI agents to work on the same project alongside people — and why coordination matters more than raw model capability.
Guides
Practical walkthroughs for setting up workspaces, projects, and agents. Published as features become generally available.
Documentation
Product and developer documentation. Not published publicly yet — early access participants get it directly.
Changelog
What shipped and when. The public changelog opens alongside general availability.
Build with your team. Build with your agents.
Bring people, projects, AI agents, context, and execution into one shared workspace.