Antigravity Cluster: Split Tasks for Elite AI Coding
Treat Antigravity as a cluster: split tasks into numbered sub-clusters (e.g., B1-B3 for backend), route to planning/fast modes and Gemini Flash/Pro models, use persistent rules, clean contexts, and parallel agents to boost quality, speed, and quota efficiency.
Task Splitting and Smart Routing Maximizes Output Quality
Break massive prompts like "build full SaaS app" into clean, numbered clusters—architecture, backend (B1, B2, B3), frontend (F1, F2, F3), testing (T1, T2, T3), verification—to avoid bloated contexts where agents mix planning, coding, styling, and debugging. This turns foggy mega-tasks into solvable sub-problems, preventing quality drops from context overload.
Route clusters by task: Use planning mode with reasoning-heavy models like Gemini 3 Pro (or partner models) for architecture, migrations, debugging, code reviews—anywhere early bad decisions cascade. Switch to fast mode with speed models like Gemini 3 Flash for low-risk execution: variable renames, lint fixes, UI tweaks, endpoint wiring. Avoid overkill—deep reasoning on trivial edits burns quota and slows workflows; batch small changes instead. Result: Faster execution, higher accuracy, sustainable usage since quotas tie to work complexity, not requests.
Persistent Rules and Context Hygiene Build Reliable Defaults
Set workspace rules/workflows/skills (project-specific over global) for reusable guidance: Embed code style, architecture prefs, constraints in always-on rules; trigger workflows for code reviews, test generation, security checks, frontend polish. This eliminates re-prompting habits, letting agents know plan structures, review standards, and test approaches upfront—upgrading long-term performance without daily prompt tweaks.
Maintain context hygiene with one conversation per lane (backend-only, frontend-only); handoff bloat via summaries like "B1-B2 done, schema finalized—implement F1-F2 only." Anchor early: Specify stack, key folders/files, no-touch zones. Feed direct artifacts (editor diffs, terminal errors) over paraphrased bugs to cut guessing. Cleaner threads reduce confusion, keeping agents focused and performant.
Parallelism, Feedback Loops, and Full Workflow Recipe
Run parallel agents for independent lanes (backend in one, frontend/testing in others) via agent manager—but only for truly separable tasks to avoid chaos; fallback to side panel for focus. Steer via feedback artifacts: Review plans/diffs/walkthroughs/screenshots early; small comments prevent drifts better than late corrections.
Recommended recipe: (1) Planning mode: Inspect repo, generate numbered cluster plan. (2) Execute one cluster—fast mode for simple, planning for complex. (3) Model-match task. (4) Leverage rules/workflows (e.g., review pre-merge). (5) Parallel lanes for independence. (6) Continuous artifact feedback. Caveats: Match available models to your tier/region; conserve free-tier quotas; tighten secure mode for sensitive work. Orchestration—not just smarter models—transforms Antigravity from average to exceptional.