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Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic

Guides have aided humanity throughout history. Prehistoric civilizations understood that the sun and the moon could be used to navigate vast distances on land and the high seas. Over time, various journeys facilitated the production of maps for better planning and faster travel time to repeat destinations. Centuries later, the introduction of the compass enabled seagoers to achieve greater accuracy in seeking unexplored destinations. And today, GPS navigation apps guide our every journey. In today’s world of agentic AI, AI agents, admittedly, have the potential to enable scalable AI adoption, transforming industries as we know them. However, an intelligent guide, agentic logic, is needed to realize this potential by fueling high agent quality, cost-effectiveness, and consequent end-user trust.

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đź”® Seven lessons for managing AI agents – Exponential View

In April 2025, we shared our seven lessons for building with AI. Many still hold. But agents have changed how we work, so the lessons deserve an update.

Agents can now work on harder tasks for longer. They plan, use tools, work without human oversight, and act on our behalf. In May, roughly a quarter of Codex users were making at least one request per month for work that would take a human eight work hours to complete. This is up from 2% in December 2025.

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Scarcity and strategy – Misreading AI the way Hollywood misread streaming

An AI-native organization needs to frame the right search space, generate many possibilities within it, cheaply eliminate weak ones, test the promising ones against reality, determine where irreversible commitment is justified and then feed the results back into the next round of search.

The advantage is created through a superior learning architecture spanning possibility to evidence to commitment.

Strategy in a world of expensive exploration is largely the discipline of making good choices with limited analysis.

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