Most CIOs Can't Define "Agentic Architecture." That's Becoming a Problem

Enterprise AI budgets are shifting from single models to multi-step agent systems, but many organizations are struggling with the architecture that supports them. A Google Research study found that adding more agents to a workflow doesn't always improve performance and can even worsen it, depending on how the agents are organized.

Most CIOs and IT leaders have differing definitions of agentic architecture, ranging from chatbots to swarms of specialized bots. This lack of shared understanding is causing organizations to make architecture decisions without a clear common goal. Researchers found that centralized multi-agent coordination can improve performance on tasks with independent sub-tasks, but degrade performance on tasks with tight sequential dependencies.

The key takeaway is that the architecture around the model, not the model itself, is crucial for successful implementation. Organizations often prioritize adding more agents as the solution, but the real differentiator is the surrounding system, including orchestration, retrieval, and decision-making about when to use multiple agents.