The Loop That Actually Ships
Most app flows are linear: a user does something, the app responds, and we stop. Agents break that line and turn it into a system that watches, decides, and tries again until the goal is met.
Diagram
Goal
↓
Sense → Decide → Act → Measure
↑ ↓
└─────────── Update ────┘With the loop closed, you get:
- Tasks that finish without constant prompts
- Recovery paths when something fails
- Behavior that adapts as the environment changes
- Less manual babysitting
Mobile-Specific Challenges
Mobile is different. In web or backend work, you can simulate most behavior locally. On mobile, the real device matters. Cameras, sensors, thermal throttling, permissions, and OEM quirks are hard to fake. That makes the loop slower because humans end up doing the device-side validation.
Closing the loop on mobile means connecting the agent to real hardware and real state. For Android, that often looks like direct access to ADB so the agent can install builds, drive the UI, capture logs, and verify what actually happens on the device.
A Real Example
I was debugging a camera focus issue in an Android app. My old workflow was:
- Ask a coding model for a fix.
- Build in Android Studio.
- Run on device.
- Reproduce manually.
- Repeat.
It worked, but it was slow and repetitive.
So I asked if the agent could verify its own changes. Once my device was connected over USB, it could run ADB commands to install builds, start the app, drive the camera flow, and capture outputs. I let it run for a couple of hours while it tried different hypotheses.
Hey, can you test this on the real device USB connected? Run whatever ADB commands you need, keep iterating until it works, and write every attempt plus screenshots into a md log file.
The result was a detailed markdown log of every experiment, complete with screenshots and what changed between attempts. I still had to steer it a few times, but it eventually found the root cause and taught me a lot about CameraX and Camera2 along the way.
Why This Matters
The compounding benefit is speed and rigor:
- The agent can execute more experiments than I can in the same time.
- Every attempt is logged, so the learning is reusable.
- The loop closes on the real device, not just on a simulated environment.
That is what makes agents powerful for mobile: not just code generation, but autonomous testing and verification on actual hardware.