MECE

MECE (Mutually Exclusive, Collectively Exhaustive) is a structuring principle originating from McKinsey consulting—used to ensure that a set of categories covers all possibilities without overlap—applied in AI agent design to organize Skills and Resolver tables cleanly.

Role in Agent Architecture

In the context of AI agent engineering, MECE is advocated as a design constraint for the resolver component of an agent system. The principle holds that if a resolver table is both dry (following DRY principles) and MECE, it becomes the optimal routing mechanism for the agent. As stated in the Inside YC AI Playbook discussion: "if you have a dry and MECE resolver table anywhere it's actually like the optimal resolver".

The practical implication is that skills should be defined so that they are mutually exclusive in scope (no two skills handle the same input ambiguously) and collectively exhaustive (every possible user intent is handled by at least one skill). This drives toward a preference for one well-parameterized skill over ten overlapping, narrowly defined ones—keeping the resolver table clean and deterministic. (Inside YC's AI Playbook, 16:44)

Origin and Context

The term is acknowledged as coming from management consulting ("a McKinsey term for making really good slide decks"), but is repurposed here as a rigorous structural property for agent skill taxonomies rather than presentation logic. (Inside YC's AI Playbook, 16:44)

Points of Disagreement

No dissenting views on MECE are present in the available material.