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The authors of the bestselling \u003ci\u003ePrediction Machines\u003c\/i\u003e can help you prepare.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eArtificial intelligence (AI) has impacted many industries around the world--banking and finance, pharmaceuticals, automotive, medical technology, manufacturing, and retail. But it has only just begun its odyssey toward cheaper, better, and faster predictions that drive strategic business decisions. When prediction is taken to the max, industries transform, and with such transformation comes disruption.\u003c\/p\u003e\u003cp\u003eWhat is at the root of this? In their bestselling first book, \u003ci\u003ePrediction Machines\u003c\/i\u003e, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in \u003ci\u003ePower and Prediction\u003c\/i\u003e, they go deeper, examining the most basic unit of analysis: the decision. 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Grounded in complex systems theory and AI research and engineering, but balanced by design and product thinking, \u003cem\u003eAGENTIFY\u003c\/em\u003e draws on work with dozens of teams building AI agents today.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePacked with lessons that outlast the weekly flux of new models and frameworks, \u003cem\u003eAGENTIFY\u003c\/em\u003e combines research depth, engineering rigor, product acumen, and design craft in one meticulous volume. \u003cem\u003eAGENTIFY\u003c\/em\u003e shows how agents differ from apps: continuous planning loops, memory, tool use, meta-learning, and world modeling. It also develops architectural patterns for autonomy and oversight. Topics include reinforcement learning, synthetic and curated training data, fine-tuning and tool formation, protocols, precision context engineering, and subagent orchestration. 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