Agent complexity is a significant issue for enterprises, arising from the interconnectedness of multiple agents and their API calls. This intricate web of interactions creates a system that is difficult to govern and understand. Adding more agents doesn't just increase connections linearly; it compounds them, making the system increasingly opaque. Consequently, AI programs often stall because humans responsible for agents lose track of their operations and permissions. The current instinct to treat agent deployment like a checklist proves insufficient for governing this complex, cascading behavior.Permissions creep is a common breakdown, where agents gain broader access over time without explicit re-approval. Ownership also becomes diluted as workflows involve multiple agents, making accountability for failures unclear. Existing governance infrastructure struggles to keep pace with the interconnected and cascading nature of agent behavior. To address this, each agent must have a distinct identity, defined scope, and a human sponsor.However, agent-level identity alone is insufficient; a broader oversight mechanism is required to track agent actions and their downstream effects in real-time. Enforcement, the ability to prevent out-of-policy actions before they occur, is also critical. Enterprises that succeed in agentic AI build both visibility and accountability to manage growing agent fleets. This approach fosters "Human-Agent Harmony," allowing scale and accountability to grow together. The true risk lies not in individual agents, but in their unpredictable interactions at scale, hindering production deployment. By solving for complexity, autonomy becomes a benefit rather than a liability.