I think the biggest shift is that long-horizon agents make memory and state management a first-class engineering problem.

A context window can remember a conversation, but it doesn't necessarily remember the work. Durable state, checkpoints, verification, and explicit progress give the agent something much more important: continuity.

I also think measuring the trajectory matters just as much as measuring the final output. An agent can eventually reach the right answer while taking a completely inefficient or risky path to get there.

The interesting challenge isn't giving agents more autonomy. It's building systems where autonomy remains observable, reversible, and aligned as the horizon gets longer.