How to Run Parallel AI Agents in Google Antigravity

Running one agent in Google Antigravity is easy. Running five at once without turning your repo into a conflict minefield is the actual skill, and it comes down to scoping.

Short answer: give each agent its own git branch and its own top-level directory, never let two agents touch the same file, and review each plan before approving edits. Antigravity supports multiple concurrent agent tasks in the Agent Manager.

Why do parallel agents break repos?

Agents are fast but blind to each other. If agent A rewrites src/api.ts while agent B adds a function to the same file, you get overlapping diffs that no automated merge handles cleanly.

The fix is not a better model. It is partitioning the work so file ownership never overlaps.

How do you scope tasks so agents do not collide?

1. Split the codebase by feature folder, not by file type. Give one agent features/auth and another features/billing.
2. Create a git branch per agent: agent/auth, agent/billing.
3. Write tasks that name the exact boundary: “Only touch files under features/billing.”
4. Assign shared interfaces — types, API contracts — to yourself, not an agent.
5. Merge branches one at a time and run tests between merges.

Agents respect explicit file boundaries far better than vague “don’t break anything” instructions.

What is the right task size for an agent?

Aim for tasks an agent can finish in 10 to 30 minutes of wall-clock time and produce a reviewable diff. Too small and you spend more time writing prompts than coding. Too large and the agent drifts from your intent midway.

Task size	Outcome
One function	Underuses the agent
One feature slice	Ideal — reviewable diff
Whole app	Drifts, needs rewrite

How do you review agent output at scale?

Read the plan first, the diff second, the tests third. Antigravity’s Agent Manager shows each task’s plan and file changes, so you can reject a bad approach before it writes 400 lines.

Two habits help. Commit before every agent run, so you can git reset --hard without losing work. And require the agent to run the test suite in its own terminal surface — that catches most broken output automatically.

When does parallelism stop paying off?

Beyond four or five concurrent agents on one codebase, review becomes the bottleneck. You can generate code faster than you can read it. At that point adding agents makes delivery slower because you spend the day triaging diffs.

Parallel agents fit teams and solo developers on multi-module projects with clean boundaries. They fit badly on small scripts, tightly coupled legacy code, or anything where one file is the whole system.

Quick Summary

• Partition work by feature directory and give each agent its own git branch.
• Never let two agents edit the same file — assign shared interfaces to yourself.
• Target feature-slice tasks that finish in 10 to 30 minutes.
• Review the plan before approving edits, and commit before every run.
• Beyond four or five agents, human review becomes the bottleneck.