As the next wave of AI innovation moves beyond individual models to coordinated ecosystems of intelligent agents, multi-agent systems (MAS) are emerging as a strategic frontier. Understanding the opportunities, risks, and architectural implications of MAS is becoming increasingly critical. 

This executive-level panel brings together leaders and experts to discuss learnings from deploying AI agents across industries and unpack what organizations need to consider before leveling up to multi-agent systems. 

• Assessing learnings from deploying individual AI agents 
• Highlighting core principles and use cases of multi-agent systems 
• Choosing the right development frameworks and simulation environments 
• Understanding governance, control, and risk management in distributed AI systems 
• Determining organizational readiness: skills, infrastructure, and data strategy considerations 
• Communication, coordination, and conflict resolution strategies among agents 
• Key design trade-offs: centralized vs. decentralized control 
• Practical lessons from real-world implementations