Tal Lev-Ami

expert panel

In AI, success is no longer determined solely by selecting the largest language model or achieving the highest benchmark score. Increasingly, organizations are deploying AI systems composed of multiple foundation models, retrieval systems, APIs, business applications and autonomous agents working together to complete complex tasks.As these systems become more autonomous, reliability becomes a systems engineering challenge rather than simply a model evaluation problem. According to the National Institute of Standards and Technology's AI Risk Management Framework, trustworthy AI requires organizations to continuously monitor, govern and manage risks throughout the lifecycle—not simply evaluate a model before deployment. Those principles become even more important as organizations adopt agentic AI capable of making decisions and invoking external tools.Members of the Senior Executive AI Think Tank, with expertise in machine learning, generative AI, and enterprise AI applications, believe orchestration is rapidly becoming the operational foundation that makes this possible. Rather than simply routing requests between models, orchestration determines how systems select models, validate outputs, monitor execution, recover from failures and balance competing priorities including latency, quality, security and cost.In the following sections, they explore the critical decisions leaders must make as AI systems become more autonomous—from improving routing and evaluation to strengthening observability, managing complexity and creating safeguards that allow organizations to scale AI with confidence.