Healthcare AI requires explainability. 'The model said so' is not a clinical rationale. Mechanistic interpretability is the research field trying to change that. What it offers practitioners today, where the gap is, and what to do in the meantime.

LLM-as-judge evaluators feel like quality assurance but behave like rubber stamps. They fail hardest on the outputs that matter most: edge cases, safety-critical errors, domain-specific nuance. What to do instead.

The orchestrator/worker pattern is the key mental model for multi-agent systems. How to structure orchestrators, spawn and manage workers, aggregate results, and avoid the coordination failures that sink most implementations.