Executive Summary We still classify machine learning systems by how they are trained. Supervised. Unsupervised. Reinforcement learning. That taxonomy made sense when models were static. Agentic systems are not. They must be classified by how they behave, not how they were trained. The Real Failure Mode: Training-Centric Thinking The classic machine learning taxonomy was built…
Executive Summary Most intelligent systems don’t fail because they lack data. They fail because they treat estimates as facts. Statistics exists to solve a very specific problem: how to reason, decide, and act when the truth is only partially visible. In modern systems, especially agentic ones, this is not a mathematical concern. It is an…
Updated December 2025 to reflect modern agentic and autonomous systems Executive Summary Dimensionality reduction is often taught as a performance optimization. That framing misses the point. At scale, systems fail not because they lack information, but because they are exposed to too much reality. Dimensionality reduction is how we compress the world into something a…
Updated December 2025 to reflect modern agentic and autonomous systems Executive Summary Most machine learning systems don’t fail because they lack intelligence. They fail because they become confident too quickly. Regularization is often described as a mathematical trick to prevent overfitting. That description is correct — and dangerously incomplete. At its core, regularization is about…
Updated December 2025 to reflect modern autonomous and agentic systems Executive Summary Most distributed systems don’t fail catastrophically. They fail quietly. Threads saturate. Queues fill. Latency stretches. Everything is still “up” — and nothing is moving. The Bulkhead pattern exists for one reason: to prevent one part of a system from consuming the oxygen of…