How to Build Team Agents

The shift from individual AI agents to "team agents" marks a critical evolution in workplace productivity, enabling shared knowledge, memory, and configuration across organizational roles. Nufar Gaspar outlines four primary archetypes for these agents: expert agents that reduce dependency on individuals, common work agents that standardize recurring tasks, bridge agents that facilitate cross-departmental handoffs, and chief of staff agents that operationalize daily operations. Successful implementation requires five core design decisions: defining the agent's scope, selecting the hosting environment, curating a shared ground truth, managing permissions, and establishing clear ownership. While individual agents remain valuable for personal tasks, team agents provide the structure necessary for scaling AI safely. Organizations should prioritize these shared tools when work flows between roles or when specific expertise becomes a bottleneck, ensuring that team-level standards and operational discipline drive the agent's development rather than relying solely on individual, fragmented workflows.