Many AI agent stacks focus on coordinating workflows (orchestration). For systems with real-world impact, a separate "control plane" is essential. This layer independently validates and authorizes proposed actions against current policies and system state, preventing unsafe outcomes that agents alone might cause.
Unlike traditional software that fails with clear errors, multi-agent systems can fail silently. A series of individually logical actions, based on slightly stale or incomplete context, can compound into a significant error that is only obvious when replaying the entire sequence of events.
As powerful AI models become commoditized, the sustainable competitive advantage will shift from model superiority to architectural robustness. Building systems with independent control planes, clear policy enforcement, and auditable execution paths will be the key long-term differentiator for agentic systems.