Effective delegation is a crucial leadership skill, now being applied to AI agents in enterprise workflows. Multi-agent systems are ideal for complex tasks, requiring AI agents to become intelligent delegators. Research from Google DeepMind highlights delegation as an intelligent process involving negotiation, contracts, and security. Four principles emerge for building AI agents that can effectively delegate tasks.The first principle is to verify delegated work through "contract-first decomposition," breaking tasks into manageable, verifiable chunks. Ideally, work can be reliably graded, but subjective assessment may necessitate human expert judgment. The second principle emphasizes being smart about cost by matching task complexity to appropriate AI models. Simple tasks should not burden expensive, powerful models, while complex ones require greater capability.The third principle, respecting sensitive data, means AI agents must adhere to boundaries and permissions, granting only the minimum necessary access for a task. Advanced cryptography, like zero-knowledge proofs, can verify work without revealing private data. The fourth principle, beware the zone of indifference, cautions against AI agents blindly complying with requests. Intelligent delegation requires "dynamic cognitive friction" to validate information and challenge ambiguous requests. This prevents subtle errors or harms from propagating through delegation chains. Ultimately, the goal is to integrate AI agents seamlessly into organizations, working alongside human experts.