AI agent autonomy is safe when it matches evidence, permissions, rollback, and human review. Learn the work-contract and verification patterns that make Claude and Cursor behave more like senior engineers.
AI agent harness engineering is the runtime around a model: context, tools, memory, evaluation, permissions, and human review. This guide explains how to design it for reliable enterprise workflows.
AI scaling laws help allocate training budgets across model size, data, and compute. This guide explains Kaplan vs. Chinchilla and why training recipes can distort a seemingly clean scaling curve.
A business-friendly explanation of the math behind generative AI: how scaling laws shape GPU budgets, data strategy, model selection, and AI competition.
After two decades in strategy consulting and AI advisory, I've witnessed numerous technological waves. Agentic AI represents something fundamentally different—not just automation 2.0, but a paradigm shift that will reshape enterprise workflows and create unprecedented market value.