Multi Agent Systems (MAS) face a critical challenge to maintain trustworthiness alongside operational efficiency as they move from theoretical development to practical implementation in essential domains. Human-in-the-loop approaches create operational bottlenecks that limit scalability, but fully autonomous systems face the risk of catastrophic failures. The proposed framework in the paper introduces a new approach to minimal human oversight through strategic intervention points that use intelligent triggers that assess risk and quantify uncertainty, detect novelty, and analyze consensus. The framework achieves scalable oversight of complex MAS through optimized information flow and reduced cognitive load that maintains critical safety guarantees. This position paper conducts a theoretical analysis of the impact of system performance while presenting applications for autonomous transportation, critical infrastructure management, and financial systems.