October 28, 2026
Taming the 'Agent Zoo': How to Scale AI Without Losing Control
In this episode of the AI Proving Ground Podcast, Jason Campagna explores how workflow design, business context and architectural choices help enterprises control agent sprawl, improve reliability and manage the economics of AI at scale.
Enterprises are gaining access to AI faster than they are organizing the knowledge and systems needed to put it to work. As agents take on business processes, that gap carries consequences for accuracy, oversight and cost.
Scaling begins with understanding the workflow: what each step requires, where human judgment matters and how results will be verified. Those requirements should guide model selection, infrastructure and the context supplied to each agent.
Context deserves particular attention. Giving AI more data can increase expense without improving its decisions. Supplying the right information at the right moment can help organizations improve accuracy while reducing unnecessary processing.
In this episode of the AI Proving Ground Podcast, Jason Campagna connects these choices to the realities of enterprise deployment. His argument offers leaders a practical starting point: examine a consequential workflow closely enough to understand what reliable execution demands, then build the architecture and controls to support it.
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About the AI Proving Ground Podcast:
AI deployment and adoption are complex. The AI Proving Ground Podcast makes them actionable. Join top experts, IT leaders and innovators as we explore AI's toughest challenges, uncover real-world case studies and reveal practical insights that drive AI ROI. From strategy to execution, we break down what works (and what doesn't) in enterprise AI. New episodes are available every week.
Featured speaker
World Wide Technology
Senior Director, AI Practice Strategy