The organizing principle is verifiability. Every agentic use case sits somewhere on a spectrum from โmachine-checkable in millisecondsโ to โrequires human taste and judgment.โ Where a use case sits on that spectrum determines how quickly RLVR-trained agents can operate autonomously, how much human oversight remains necessary, and how fast the compound flywheel spins.
The market context: AI agents are projected to grow from $7.84 billion in 2025 to $52.62 billion by 2030 (46.3% CAGR). Gartner predicts 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025. Agentic AI could drive approximately 30% of enterprise application softwarerevenue by 2035 โ surpassing $450 billion. These arenโt hypothetical projections. 72% of medium- and large-sized enterprises already use agentic AI, and an additional 21% plan to adopt it within 2 years.
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Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
