The interesting part of agent loops isn’t how to keep an agent running. It’s knowing when the work is understood well enough to let it run.

Peter Yang, interviewing Claude Code’s Thariq Shihipar on Behind the Craft, asks about Claude Code’s new loop, goal, and workflow features. Shihipar describes /goal less as an autonomy switch than as a signal that the uncertain work has already been done:

/goal is great when you have a complicated task and need to make sure it is done at the end. It’s the user indicating, “I’ve done enough specification and exploration. I understand the problem space. Just go execute on it, and if you run into something, fill it in.”

That puts a useful boundary around loop engineering: don’t ask the agent to power through ambiguity you haven’t investigated. Shihipar treats planning as the work of reducing that ambiguity:

We often talk about plans as one shot: you plan, then you do something, and that’s it. But planning is an iterative process of exploring, investigating, and finding out what you don’t know and what you want.

A few minutes later, he gives that process a better name:

I like to call it getting rid of your unknowns. With almost any task, there’s a lot you don’t know—either how things work or what you want. It’s very iterative. You don’t write it all down once and then implement it. There are many steps and different passes.

Implementation doesn’t end that process. Shihipar asks the agent to keep notes about what it discovers while building, then feeds those discoveries back into the specification:

The model can find things that it—or you—didn’t anticipate during implementation. I ask it to keep implementation notes as it goes: what did we not expect about this implementation? Once we have that, we can respec if needed. It’s much less one handoff from specification to implementation and more a back-and-forth process.

That’s the more useful loop: explore until “done” is concrete, build the smallest version that can expose what the plan missed, then revise the plan from what the build teaches you. Longer-running agents are the consequence, not the point.

Quotes lightly edited for clarity.