Model selection is a strategic governance challenge. Foundation models are not interchangeable utilities but exhibit distinct behavioral tendencies, interaction patterns, and risk profiles that must be actively managed. AI agents are rapidly becoming embedded in enterprise technology stacks and operational decision-making. However, the supporting infrastructure required to govern, constrain, and reliably coordinate these agents across complex environments remains underdeveloped. The core challenge is no longer simply improving agent capability but ensuring the long-term stability, predictability, and controllability of agentic systems operating over extended time horizons. Research from Emergence.ai highlighted in this report shows that foundation model selection significantly influences the quality and safety of long-duration agent behavior. Simulations demonstrate that model transparency, explainability, and auditability are no longer optional technical features but operational necessities. Effective deployment therefore requires far closer collaboration between model providers, professional services partners, and business and technology leaders to align model behavior with organizational resilience, governance, and long-term strategic objectives. The findings underscore a critical shift in advanced computing priorities toward maintaining control of autonomous systems. This Expert View highlights key findings from the simulations, and PAC outlines five strategic lessons for effective agentic governance and deployment.
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