AI
Most AI coding assistants (including earlier Copilot modes) work at the point of your cursor: you type, it suggests, you accept or reject inline. Antigravity's agent-first model is structurally different — you hand it a task description, and it works across your codebase asynchronously, touching multiple files without you watching each edit happen. That's a genuine capability upgrade for multi-file refactors, but it also means the review workflow has to change: you're reviewing a completed body of work, not approving suggestions one keystroke at a time.
Working with Antigravity's autonomous workflow
1. Write a task description an agent can actually execute against. Because the agent works asynchronously across files, a vague prompt produces a vague (and possibly wrong) multi-file change — be as specific about scope and constraints as you would in a written spec.
2. Use the Agent Manager to run and compare multiple attempts. For non-trivial changes, spawning more than one agent run on the same task and comparing their artifacts is often faster than iterating on a single agent's first attempt.
3. Review the artifacts before the diff. The task list and implementation plan an agent generates tell you what it believed it was doing — read that first, since a plausible-looking diff built on a wrong plan is easy to miss on a code-only review.
# Example task description for an Antigravity agent run
# -- specific scope and constraints, not a vague one-liner --
Refactor the `UserRepository` class in `src/db/user_repo.py`
to use the new `AsyncSession` pattern already established in
`src/db/order_repo.py`. Update all 4 callers listed in
`src/services/user_service.py`. Do not change the public
method signatures. Add unit tests mirroring the existing
tests in `tests/test_order_repo.py`.| Interaction Model | Editing Scope | Review Unit |
|---|---|---|
| Inline autocomplete (traditional Copilot) | Text at the current cursor position | Per-suggestion accept/reject |
| Antigravity agent-first workflow | Multiple files across the codebase, asynchronously | Full task artifact (plan, diff, test results) reviewed together |
The trade-off is real: letting an agent work unattended across files is faster for well-scoped, well-specified tasks, and slower to trust for ambiguous ones. Treat the specificity of your task description as the main lever you control — a precise scope with named files and explicit constraints gets you a reviewable result; a vague one gets you a plausible-looking diff you have to reverse-engineer.
Practical Challenge
Give Antigravity a multi-file refactor task with explicit file names and constraints (like the example above), then review the generated task list and plan artifact before looking at the diff — note whether the plan surfaced anything the diff alone wouldn't have.
Concept Check
Sources & Further Reading
- Google Developers Blog: Build with Google Antigravity — the official announcement covering the Agent Manager, artifacts, and asynchronous multi-file editing model.
- Google Antigravity — Wikipedia — background on the release date, architecture, and underlying model support.
- Codecademy: How to Set Up and Use Google Antigravity — a practical setup and first-task walkthrough.