The artificial intelligence landscape is undergoing a fundamental shift from isolated chatbot features to sophisticated multi-agent systems that can orchestrate complex tasks across organizations. In a wide-ranging discussion on practical AI strategy, industry leaders explored how companies are moving beyond single-model deployments toward architectures that coordinate fleets of specialized AI agents—each optimized for different functions and powered by a mix of open and proprietary models. This architectural evolution reflects growing enterprise recognition that monolithic AI solutions fall short of real-world business needs. Central to this transition is the concept of agent harnesses—orchestration layers that manage communication, task allocation, and coordination between multiple AI agents. These systems represent a maturation of AI deployment from "chat interfaces" to autonomous agents capable of taking meaningful action within defined parameters. Organizations are grappling with critical decisions around model selection, including the choice between open-source and closed commercial models, each carrying distinct tradeoffs in cost, performance, and vendor dependency. The discussion highlighted how these architectural choices ripple through entire organizations, affecting everything from compliance to operational flexibility. Practical adoption requires organizations to understand not just the technology, but the strategic implications of their choices. As enterprises begin implementing agentic AI systems, they must navigate concerns about vendor lock-in while simultaneously managing the complexity of coordinating multiple models and agents. Industry practitioners are now focusing on concrete strategies for transitioning existing workflows to multi-agent architectures, with clear guidance on where to start and how to build sustainable AI infrastructure for the years ahead.