Multi-agent orchestration is a process that involves automating and coordinating multiple AI agents to work together towards a common goal, allowing them to specialize in specific tasks and master their work with high accuracy. This approach expands the capabilities of AI by enabling agents to focus on different contexts and communicate with each other to get work done, creating effective workflows such as recruiting, customer support triage, and financial operations management. However, multi-agent orchestration also increases the attack surface and introduces challenges such as permissions, memory, and observability, which can be addressed through the use of AI orchestration frameworks that ensure secure communication between agents and provide features like memory partitions, purpose-bound queries, and redaction/transform hooks. Additionally, observability is crucial in multi-agent systems to monitor and visualize paths between agents, track who called whom, and identify bottlenecks, making it essential for compliance, security, and debugging purposes. Effective multi-agent orchestration platforms, such as Credal, can support agent-to-agent communication protocols like A2A, provide agent registries for discovery, and offer features like cost and performance controls to minimize the likelihood of outages.