Beyond Chatbots: A Practical Guide to Agentic Coding with Antigravity

The Paradigm Shift: From Chatbots to Workspace Agents

In the early days of AI assistance, developers got used to a highly manual workflow: write a prompt, receive a code block, copy it, paste it into the editor, run the build command, discover a compiler error, copy the error, and paste it back into the chat box. This loop is slow, repetitive, and introduces human error during integration.

Antigravity, designed by the Google DeepMind team, changes this paradigm entirely. It is not just a chatbot; it is an agentic coding partner. Operating with direct access to your local workspace, Antigravity can search files, edit code across multiple non-contiguous locations, execute command-line tasks, and even delegate complex sub-tasks to specialized background agents.

However, with great power comes the need for structure and safety. This guide walks you through the core workflows of Antigravity, showing how to use its planning mode, execute complex refactors, and maintain complete control over the safety of your codebase.

Understanding the Antigravity Agentic Lifecycle

When you issue a command or request to Antigravity, it doesn't just start writing code immediately. Instead, it follows a structured lifecycle designed to ensure correctness and prevent destructive modifications. This lifecycle is broken into four distinct phases: Research, Plan, Execute, and Verify.

First, the agent researches your codebase to understand existing conventions, dependencies, and file structures. Next, it writes a detailed implementation plan outlining exactly what will change. Once you approve the plan, it executes the changes step-by-step using a task checklist. Finally, it runs verification commands (like tests or lints) to ensure everything compiles and runs correctly.

Step 1: Codebase Research and Targeted Queries

Before writing a single line of code, Antigravity uses advanced search tools to inspect your workspace. Instead of uploading the entire codebase (which wastes context and budget), the agent uses ripgrep-powered searches to find exact matches or searches directories to identify relevant components.

For example, if you ask the agent to add a new API endpoint, it will search for existing route handlers to match their structure, and look at the project's dependency manifest (like `package.json` or `go.mod`) to see which libraries are available.

You can assist the agent during this phase by providing specific file paths or pointing it to similar implementations in your prompt.

# The agent uses ripgrep under the hood to find files importing routes
grep -rn "import.*routes" ./src/

Step 2: Creating and Approving the Implementation Plan

For any complex changes, Antigravity enters 'Planning Mode'. During this phase, it generates a special document called `implementation_plan.md` in your conversation's brain folder. This plan outlines the goal, lists the specific files that will be modified, created, or deleted, and details how the changes will be verified.

The agent will halt execution and wait for your explicit approval of the plan. This is a crucial safety gate: you can review the proposed approach, ask for adjustments, or correct design assumptions before any files are modified. Once you give the go-ahead, the agent transitions to execution.

# Implementation Plan: Add User Profile Endpoint

## Proposed Changes

### Backend API
#### [NEW] [user.go](file:///d:/codex/website/api/user.go)
- Create handler for GET /api/v1/user/profile

#### [MODIFY] [routes.go](file:///d:/codex/website/api/routes.go)
- Register user profile route in main mux

## Verification Plan
- Run `go test ./api/...` to verify routing

Step 3: Precise Code Edits and Multi-File Changes

During execution, Antigravity tracks its progress in a dynamic checklist (`task.md`). It uses highly optimized tools to make modifications. Unlike generic chat assistants that output full files and force you to download them, Antigravity uses targeted replacement tools.

For single continuous updates, it uses block replacements. For complex refactors that touch multiple separate parts of a file simultaneously (e.g., adding an import, registering a route, and defining a handler), it uses multi-replace tools. This prevents the agent from rewriting unrelated sections and ensures that code comments, formatting, and surrounding context remain fully intact.

Every write operation is done locally on your machine, allowing you to instantly see changes in your IDE and revert them using git if needed.

Step 4: Executing Commands and Verifying the Changes

No feature is complete until it is verified. Antigravity can execute commands on your behalf—such as running unit tests, starting development servers, or building Docker images. However, to protect your machine, all shell commands run inside a confirmation sandbox. The terminal command will only execute after you explicitly click approve.

Once the command runs, the agent reads the output, detects any failures or lints, and automatically applies self-correction. When all verification checks pass, it creates a final `walkthrough.md` summarizing the changes, making it easy for you to review and commit the changes to your repository.

# Running local tests to confirm the build is passing
npm run test

Working with Subagents for Parallel Tasks

One of Antigravity's most powerful capabilities is its ability to spawn autonomous subagents. If you have a complex task that requires simultaneous research and coding, the main agent can define a specialized subagent (such as a 'Codebase Researcher' or a 'Database Debugger') and delegate work to it.

These subagents run in parallel in the background, utilizing their own tool configurations to search files, read docs, or test APIs, and report back to the main agent once completed. This multi-agent coordination keeps the main conversation context clean and allows for much faster problem-solving.

Safety Guidelines and Best Practices

While Antigravity is built with safety in mind, it is still an AI assistant that acts under your guidance. Always inspect shell commands before approving them, especially if they make network requests or delete files. Keep your changes version-controlled so you can easily diff and discard edits if the agent takes a wrong path.

Additionally, treat the agent as a pair programmer: review its implementation plans carefully, point out architectural preferences early, and break large goals down into smaller, bite-sized tasks. With this collaborative mindset, Antigravity will dramatically accelerate your development speed while keeping your codebase clean and reliable.