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◉ Antigravity Basics/2026-06-19Advanced

When CLI, Desktop, and SDK Share One Agent Harness: Designing for Consistent Behavior

Now that Antigravity's CLI, desktop, and SDK share one agent harness, here is how to separate what stays consistent from what differs by environment, and how to align behavior with smoke tests and a version-tracking habit.

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When Antigravity CLI consolidated as the successor to the Gemini CLI, one line in the official explanation caught my attention: because Antigravity CLI shares the same agent harness as the desktop version, improvements to the core agent automatically reach every surface.

That is convenient, but from a designer's seat it is also a change that calls for new care. As an indie developer, I often move a workflow I prototyped interactively on the desktop onto scheduled CLI runs, so I needed to be clear about how far I can expect identical behavior. Here is how I think about aligning the behavior of the CLI, desktop, and SDK in this shared-harness era.

What "sharing one harness" means

An agent harness is, roughly, the core behavior of how an agent reasons, how it calls tools, and how it judges results. The crux of this change is that this core becomes common across the CLI, desktop, and SDK.

So the quirks of agent judgment you observe on the desktop will, in principle, show up the same way in the CLI. Put the other way, you can carry behavior confirmed on the desktop into automated CLI runs to a meaningful degree.

But "in principle" is the catch. What is shared is the core behavior, not the environment around it. Conflate the two and you get the classic "it worked on the desktop but fails in the CLI."

The benefit: one improvement reaches every surface

The biggest benefit of a shared harness is that core improvements arrive on every surface automatically. The old split where "only the CLI is stuck on outdated behavior" becomes far less likely.

In my operation this pays off when I take a prompt structure I refined interactively and move it straight into a scheduled run. I confirm the agent's response tendencies on the desktop and carry those learnings into the CLI scheduled task. When the core is common, less is lost in that transfer.

Even when the surface for learning differs from the surface for production, a shared foundation keeps knowledge from going to waste. For indie work, where one person shuttles between experiment and operation, that is a welcome property.

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WHAT YOU'LL LEARN
✦How to separate what a shared harness keeps consistent from what environment differences break
✦A lightweight smoke test that checks behavioral parity across surfaces
✦A habit for tracking versions when core improvements auto-propagate to every surface
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