After putting Google Antigravity through its paces for the last three weeks, I wanted to share some observations from the perspective of a long-term software engineer (39 years in the industry). There is a lot to love here, but the “inner loop” experience on Windows 11 still needs some polish before it’s ready for prime time.
The Good: Agent Rules & Complex Orchestration
Antigravity shines when it comes to complex task handling, provided you don’t treat it as a “black box.” I’ve found that the quality of your Agent Rules is the primary driver of success.
Pro Tip: If you invest time in crafting granular rules, the tool’s reasoning and output quality jump significantly. It requires patience and a clear guidance strategy, but the results are superior to other agents I’ve tested.
The Friction: Windows 11 Stability & Inference Hangs
I’m seeing some recurring stability issues specifically on the Windows 11 build that seem model-independent:
Inference Deadlocks: The app frequently “goes out to lunch” right at the moment inference should begin. The UI remains somewhat responsive, but the agent hangs indefinitely.
The “Spinny Circle of Doom”: Closing and reopening the workspace often leads to a perpetual loading state. Currently, the only reliable fix is a full restart of the Antigravity instance.
Manual Retries: I frequently encounter “Agent Error” prompts. In almost every instance, clicking “Retry” resolves the issue immediately.
Feature Suggestion: Given how high the success rate is for manual retries, could we get an automatic retry logic implemented under the hood? It would significantly smooth out the UX.
My Environment & Workflow
For those interested in integration, I’m currently running Antigravity “wired” to Notion using native MCP (Model Context Protocol) interfaces, alongside my standard GitHub sync. The context retrieval via MCP is powerful, but it highlights the need for a “Care and Feeding” guide for these agents.
I’m considering sharing my preset agent rules if the community is interested. It feels like we need a more formalized set of best practices for “guiding” these agents through long-running tasks.
Anyone else seeing these specific hangs on Win11, or found a more elegant workaround than a full restart?
I was wondering if you would elaborate a little more about the types of rules that fit here.
I thought that the agent was supposed to self improve through “knowledge” items but I see it making the same mistakes over and over even when I tell it to try and harden its knowledge.
One of the reasons I use Notion is that the internal knowledge base system doesn’t seem to work. I never, ever, see it updated. Putting it in Notion ensures I have a copy later on.
This is all I ever put in “Gemini.md.”:
Read and follow the rules in “C:\Users\furre.gemini\Agent and Manager Rules.md”
If the file cannot be read or verified by quoting first and last lines, STOP and report the exact error.
Do not paste the full rules back into chat. Apply them.
And in Agent and Manager Rules - some examples
Rules file integrity (absolute)
This rules file MUST NEVER be deleted, moved, renamed, overwritten, or modified.
If changes are needed, create a new versioned rules file and leave this file unchanged.
This rules file is exempt from any migration/cleanup/deletion steps elsewhere.
Knowledge Base (Notion) is source of truth
Load Notion access config from: C:\Users\furre.gemini\antigravity\mcp_config.json, then connect to the Antigravity KB Notion database.
Search the KB before planning/coding; reference existing KB entries when applicable.
Organize entries by major topic; create new categories when needed.
For project docs found on disk: copy verbatim into the KB; add a short summary at the top without altering the verbatim body; after verifying the Notion copy is complete, you MAY delete the migrated on-disk file (never this rules file).
Engineering posture
Be deliberate. Do not rush.
Do not assume. Verify by inspection, docs, logs, and reproducible tests.
Default priorities: maintainability, security, reliability, then performance (unless instructed otherwise).
Coding safety baseline
Initialize variables before use (especially loop/state variables).
Use logging to diagnose and verify. Production logging MUST include retention/rotation so logs cannot grow without bound.
Testing + debugging discipline (with ladder)
Prefer automated tests. Use logs to reproduce, isolate, and confirm fixes.
If a test seems to require human interaction, attempt instrumentation/simulation via logging and scripted inputs.
Debugging ladder (use this order):
Reproduce with a minimal, isolated case (reduce variables).
Inspect logs and add targeted instrumentation where signal is missing.
Verify assumptions against docs/source (no guessing).
If applicable, step through with a debugger or add asserts/sanity checks.
Create a minimal repro artifact (script/test) and document it.
Timebox attempts; if unresolved, escalate with: repro steps, logs, hypotheses tried, and next best experiments.
If external libraries/APIs are involved, research known issues and current docs online.
Set stop conditions (max attempts/timebox). If still stuck, escalate with diagnostics and a minimal repro.
Documentation hygiene
Update docs when behavior, interfaces, config, or deployment steps change.
After doc updates, write a “lessons learned” note into the KB.
UI and assets
For executable programs, consider icons from: C:\Users\furre\OneDrive\Documents\Icons (and subfolders).
Build and packaging
Scripts are acceptable for early prototyping; production-quality deliverables SHOULD be executables with repeatable build steps.
API discipline
Before implementing an API integration, read official docs end-to-end and confirm current versions, auth, request/response formats, limits, and edge cases.
If implementation is unusually difficult, pause and evaluate alternative tools/libraries/approaches.
Workspace hygiene
Close browser tabs/windows when finished.
Final review
After substantial changes, perform an end-to-end review: correctness, security, error handling, logging, docs, tests, regressions.