Designing Agentic AI for Multi-Team Collaboration

Agentic AI works best when it connects teams, not when it sits inside one of them. Most collaboration problems don’t happen in a single workflow, they happen when work moves between teams. Context can get lost, updates can slip through the cracks and leadership can end up looking at multiple slightly different versions of progress, each shaped by its own idea of what “done” looks like.

Agents built for a single team can make these gaps worse, with outputs drifting, reporting slipping and information failing to move cleanly between teams. The tech might work perfectly, but the organisation around it doesn’t. The biggest impact comes at the points where work moves between teams, where things need to flow smoothly.

In a custom platform, agents are at their best when they span teams and support structured coordination. They can pull updates together, track dependencies, standardise reporting and keep context intact. They’re not there to replace judgement, but to take the busywork off people’s plates so they can focus on decisions instead of chasing information.

Take a product launch across marketing, design and engineering. Each team moves at its own pace, often in different tools. An agent can pull updates together, flag blockers and produce a consistent weekly summary for leadership. Teams still own delivery, leaders still decide priorities and the agent keeps work flowing so everyone is on the same page.

What makes this work is clarity, again. Before building anything, map how information actually moves between teams. Where are decisions made? Where are approvals needed? Where does context drop? Define escalation paths, agree what a good output looks like and be clear about where human oversight matters, especially for financial, regulatory or reputational decisions. Agents should support those moments, not replace them.

At Studio Graphene, we’ve found agents work best when deliberately placed at points where teams interact. Clear responsibilities and predictable outputs matter more than complexity. When visibility improves, alignment improves, teams spend less time coordinating and leadership sees evidence rather than interpretation.

To reiterate, the biggest impact doesn’t come from individual teams working in isolation. Designing agents for multi-team collaboration means you’re helping your organisation move information and make decisions more smoothly, not just automating more tasks.