Article
Why teams reach for multi-agent architectures
A single, general-purpose AI agent can handle simple tasks well, but complex enterprise workflows - like end-to-end customer onboarding or multi-step financial analysis - often benefit from specialized agents that each excel at one part of the job and hand off to each other.
Core building blocks
Effective multi-agent systems need clear tool interfaces, a shared context/memory layer, and an orchestration layer that decides which agent handles which step. Just as important is observability: being able to trace exactly why an agent made a decision.
Governance matters as much as capability
As agents gain more autonomy, evaluation and guardrails become critical. Our Agent Studio platform was built specifically to give enterprises this combination of orchestration power and production-grade governance.