As announced in this and the .
The orchestration tax
The approach I am proposing also solves another pressing issue for modern engineering teams: cognitive overload. As to using AI for coding. The time developers save generating code is often pushed onto reviewers as large, complex PRs, causing context switching and cognitive fatigue.
By offloading the tedious "first pass" search to an Antigravity agent, human reviewers can mitigate this tax and focus on high-level architecture and safeguarding quality.
Why we need automated agentic code reviews
AI-generated code can be deceptively good. It is often clean, well-documented, and syntactically correct. This makes it harder for human reviewers to spot subtle logical bugs or security vulnerabilities that might not be immediately obvious.
In a large codebase, manually verifying every change is simply not feasible. This is why we need autonomous agents that can step into the codebase and analyze it from a fresh perspective.
But if a developer used an LLM to generate the code, how can we trust another AI to find the bugs? The answer lies in the agent architecture and context separation.
Developers might write code using any tool — whether it's CLI, an IDE extension, or various models like Gemini 3.5 Flash or Gemini 3.1 Pro. The reviewer, however, is a managed Antigravity Agent running via a separate SDK integration. This agent has a specialized, low-freedom persona and strict system instructions that force it to act as an adversarial code auditor rather than a developer. Furthermore, it operates in an isolated environment. Because it has a different system prompt, safety guardrails, and context boundaries, the agent reviews the changes with a completely fresh perspective, catching logical bugs and vulnerabilities that the original generator might miss.
To demonstrate it in practice I created an agentic review pipeline, which:
- Leverages a managed
You can find the code at is a composite GitHub Action that runs the Google Antigravity SDK (
google-antigravity) directly on the GitHub Actions host runner.
Why run on the host instead of a container?
By running directly on the host, the Antigravity SDK has access to the host's Docker daemon. This allows the SDK to spawn Docker-based MCP servers (like the GitHub MCP server) to read files, run tests, and post reviews.
Sub-containers should ideally run with restricted network access and read-only filesystems where possible to prevent an LLM from being tricked into executing arbitrary destructive commands. The limited set of permissions is handled in the GitHub Action configuration ().
Moreover the workflow is explicitly protected from running automatically on forks, preventing unauthorized code execution. The automated review job will only run if the pull request originates from the same repository ().
Demonstration walkthrough
The demo below shows the action triggered by a new PR:
Implementation: How to install the action in your repo
Let's walk through the setup process step-by-step.
Step 1: Add your API key to GitHub secrets
The action requires a Google Gemini or Antigravity API key to authenticate language model interactions.
and use it as a template to build your own custom, agentic GitHub Actions. By modifying the safety policies, custom tools, or prompts inrun_agent.py, you can tailor the agent's review behavior to your team's specific codebase, style guidelines, and compliance rules.
For a full workflow template supporting both automated PR reviews and comment-triggered reviews, refer to the .
- Read the documentation to customize the prompts and mode.
- Feel free to fork the repository and build your own automation.
Acknowledgments
This project was inspired by the
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