From an enterprise architecture perspective, AI agents should collaborate only when there is a clear business outcome that requires shared context, data, or orchestration across processes. Agent-to-agent interaction must be intentionally designed, governed by role-based access, least-privilege principles, and end-to-end auditability rather than convenience.
Conversely, agents should remain isolated when they operate across sensitive domains such as HR, Finance, Legal, or regulated data workloads, where security, compliance, data residency, or segregation-of-duties requirements outweigh the benefits of collaboration.
The key architectural decision is not "Can these agents share information?" but "Should they?" Every integration point increases both value and risk. Therefore, architects should establish clear trust boundaries, define ownership of data and actions, and enable collaboration only where it delivers measurable business value while maintaining governance, compliance, and operational control.
