As AI agents gain autonomy and start operating in interconnected environments, new classes of failure are surfacing that traditional security models can't predict or prevent. Systemic risk in multi-agent AI refers to the way small issues can snowball into large-scale failures when agents interact. Emergent behaviors arise when otherwise functional agents start influencing each other in unpredictable ways, even well-performing models can spiral out of control without proper coordination and monitoring. The MAESTRO framework provides a comprehensive multi-layer approach for threat modeling in agent systems, addressing vulnerabilities at each architectural level and helping teams evaluate multi-agent chains for coordination risks. Implementing effective strategies for model security and runtime monitoring is essential to catching emergent risks in multi-agent systems that may not be visible during design-time analysis. Real-time monitoring provides the necessary visibility to detect and respond to subtle breakdowns before they escalate, maintaining reliability and safety across dynamic, agent-based workflows running in production environments.