---
title: AI Agent Android Automation
description: Use Luotsi when you need AI agents to inspect, steer, and replay real Android device workflows over adb.
---

If you searched for AI agent Android automation, the practical question is whether your agent can inspect real device state, act through a host-side control loop, and recover with replay artifacts. That is exactly the slice Luotsi is built to cover.

## What Luotsi gives an agent loop

- `inspect` for structured JSONL exploration of the current screen.
- `view` when a human still wants the mirrored device while the agent consumes stream state.
- `run` when the flow is stable enough to codify as a JSON scenario.
- `replay` when the device session is over and the next question is summarization, search, or triage.

## Recommended starting path

```bash
luotsi doctor --device <serial> --fix
luotsi inspect --device <serial>
luotsi scenario-validate --path scenarios
luotsi run --path scenarios --device <serial>
luotsi replay packet --artifacts ./artifacts/<run>
luotsi replay packet --artifacts ./artifacts/<run> --check
```

This progression keeps the agent loop on the host machine: explore first, codify second, replay after the run. Start replay with `packet` and `packet --check` so the agent gets the At a Glance summary, failure snapshot, primary failure, recommended next action, and first-minute checklist before deciding whether to open, summarize, search, draft, or rerun.

When the starting point is Android CLI Journey-style intent, use [Evidence-Backed Android Journeys](../../core-workflows/evidence-backed-android-journeys/) to keep natural-language flow authoring separate from Luotsi's reviewed scenario and replay evidence path.

## Strong fit when

- The agent needs real-device Android state rather than browser DOM state.
- You want machine-readable JSONL sessions instead of a window-only control path.
- You want the same runtime to support human debugging, agent exploration, and CI scenarios.
- You need artifacts after the run, not just a final pass or fail.

## Weaker fit when

- The workflow never leaves a browser automation surface.
- You need a cloud platform to supply both the devices and the orchestration model.
- You only care about scripted end-to-end runs and not agent-driven exploration.

## Open these next

- [AI Agent Workflows](../../core-workflows/ai-agent-workflows/)
- [Evidence-Backed Android Journeys](../../core-workflows/evidence-backed-android-journeys/)
- [Output Envelopes](../../reference/output-envelopes/)
- [Replay and Artifacts](../../core-workflows/replay-and-artifacts/)
- [Scenario Playbooks](../../reference/scenario-playbooks/)
