Build anything with Augment Code, here’s how

Key Concepts

• Augment: An AI coding tool designed for senior developers, emphasizing code quality and reliability.
• Indexing: Augment's method of creating vector embeddings for each file in a codebase, enabling personalized and context-aware AI assistance.
• MCPs (Managed Code Plugins): Integrations with external tools and services that extend Augment's capabilities, such as documentation retrieval, browser automation, and sequential thinking.
• Remote Agents: Asynchronous AI agents that work on tasks in the cloud, allowing developers to offload work and review results later.
• Context Engineering: The process of providing Augment with relevant information, guidelines, and rules to improve its understanding of the codebase and project requirements.
• Agent Mode: An Augment mode where the AI autonomously executes tasks, making decisions and performing actions without constant user intervention.

Augment: An AI Coding Tool for Professionals

David Andre introduces Augment as an AI coding tool favored by senior developers due to its focus on code quality, reliability, and efficient handling of large codebases. Unlike tools designed for "vibe coders," Augment is built for professional software development.

Performance and Integrations

• Speed: Augment boasts three times faster inference speeds compared to competitors.
• Scalability: It can process thousands of files per second and supports codebases exceeding 100 million lines of code.
• Integrations: Augment offers integrations with over 100 MCP tools.

The Index: Augment's Secret Weapon

• Vector Embeddings: Augment creates vector embeddings for each file in the codebase, enabling context-aware AI assistance.
• Personalization: Each developer on a team has a separate, personalized index.
• Superior Indexing: Augment's indexing capabilities are highlighted as a key differentiator.

MCP Integrations: Extending Augment's Capabilities

• One-Click Setup: Augment simplifies the process of connecting to MCPs with a one-click setup for many integrations.
• Extensive Library: It supports over 100 different MCP integrations.

Remote Agents: Asynchronous AI Task Execution

• Cloud-Based Execution: Augment allows launching multiple remote AI agents that work on tasks asynchronously in the cloud.
• Independent Environments: Each agent operates in its own secured cloud container, checking out the code, indexing it, and performing the assigned work.
• Offline Progress: Agents continue working even when the developer is offline, enabling review and merging upon completion.

Building with Augment: A Step-by-Step Guide

Installation and Setup

1. Website Access: Navigate to Augment's website (link provided in the video description).
2. VS Code Integration: Choose the VS Code integration option.
3. Account Creation: Create an account (a 14-day free trial is available).
4. VS Code Extension: Install the Augment VS Code extension.
5. Repository Opening: Open a folder containing a code repository.
6. Extension Activation: Locate and activate the Augment extension in VS Code (via the extensions view).
7. Augment Chat: The Augment chat interface will open, serving as the primary interaction point.

Utilizing Remote Agents

1. Agent Selection: In the Augment chat, select "remote agent" from the dropdown menu.
2. Repository Selection: Choose the desired GitHub repository (connect GitHub in settings if necessary).
3. Branch Selection: Select the branch for the remote agent to work on.
4. Setup Script: Provide a setup script specific to the repository (e.g., installing requirements, running npm install).
5. Prompt Input: Enter a prompt describing the task for the remote agent.
6. Agent Dispatch: Click "run" to dispatch the remote agent to begin working in the cloud.

Example Task: Changing the autoload interval in the frontend to every 5 minutes before implementing websockets.

Building a New Project from Scratch

1. Empty Folder: Start with a completely empty folder.
2. Project Details File: Create a file named project_details.md to outline the project's overview.
   • Example Project: A VIP coding Google Form builder, allowing users to chat with an AI to create usable forms.
3. Indexing: Augment automatically indexes new files, providing context for subsequent interactions.
4. Agent Mode Activation: Switch to "agent mode" and enable "auto" for maximum autonomy.
5. Prompt Execution: Run the first prompt to establish the backend foundation.

Example Prompt: Creating the backend foundation, including file structure and repository setup.

Context Engineering: Customizing Augment's Behavior

1. Settings Access: Open the settings menu (hamburger icon).
2. Rules and Guidelines: Navigate to "rules and guidelines."
3. User Guidelines: Paste a general prompt into the user guidelines to apply to all chat and agent interactions.

Example User Guidelines: A detailed prompt used for hundreds of hours of building with AI tools.

Enhancing Prompts

• Prompt Improvement Feature: Augment offers a feature to automatically improve prompts, making them more concrete and specific.

Example: Using the prompt improvement feature to refine a vague prompt about installing dependencies.

Building the Frontend

• React App Creation: Use Augment to create a React app with a two-column layout.
• OpenAI Integration: Integrate the OpenAI API (GPT-4.1 model) for form creation.

Example Prompt: Creating a React app with a two-column layout for form creation and AI chat.

Connecting to a GitHub Repository

1. Repository Creation: Create a new GitHub repository for the project.
2. Local Initialization: Initialize a Git repository in the local project (git init).
3. Remote Connection: Connect the local repository to the GitHub repository (git remote add origin <repository_url>).
4. .gitignore Creation: Use Augment to generate a comprehensive .gitignore file.
5. Staging and Committing: Stage the changes (git add .) and commit them (git commit -m "Initial commit").
6. Branch Renaming: Rename the branch to main (git branch -m main).
7. Pushing to GitHub: Push the changes to GitHub (git push -f origin main).

JSON Parsing and Form Rendering

• AI-Powered Form Generation: Utilize GPT-4.1 from OpenAI to output JSON, which is then parsed to create form elements.

Example Prompt: Implementing JSON parsing and form rendering logic.

API Key Integration

1. OpenAI Platform Access: Log in to platform.openai.com.
2. API Key Creation: Create a new secret key in the API keys section.
3. Key Storage: Store the API key in the .env file.

Error Handling and Debugging

• Logging: Add detailed logging to the backend to identify and diagnose errors.
• Testing: Implement error handling and test the application thoroughly.

Example: Adding better logging to the backend to troubleshoot a 500 internal server error.

Improving the Design

• Styling Prompts: Use prompts to improve the frontend design and styling.

Example Prompt: Making the frontend look more modern.

MCP Integration Examples

• Context 7: Provides package documentation.
• Playwright: Enables browser automation for testing and debugging.
• Sequential Thinking: Enhances reasoning abilities.

Creating Custom Rules

1. Rule File Creation: Create new rule files (e.g., design.md, error_fixing.md).
2. Rule Definition: Define specific rules and guidelines within each file.
3. Trigger Selection: Choose a trigger for each rule (manual, automatic, or always).

Example Rules:

• Design: Favor simple layouts, keep components pure and predictable (auto trigger).
• Error Fixing: A process for fixing errors (always trigger).

Remote Agent Results and Debugging

Autoload Interval Change

• Successful Implementation: The remote agent successfully changed the autoload interval to every 5 minutes.
• Pull Request Creation: A pull request was created for the change.
• Comment Updates: Relevant comments were updated, even in files where no code changes were needed.

Full Screen Chat Mode

• Pull Request Creation: The remote agent created a pull request for adding full screen chat mode.
• Initial Implementation: The agent laid the foundation for the feature, including adding a button and some state management.
• Debugging with Playwright: Augment used the Playwright MCP to debug the frontend in real time, clicking buttons, taking screenshots, and analyzing console logs.
• Autonomous Debugging: Augment autonomously identified and fixed issues, such as the right-hand side not collapsing when the full screen button was clicked.

Conclusion

David Andre concludes that Augment is a powerful AI coding tool that can significantly improve developer productivity and code quality. Its key features include efficient indexing, MCP integrations, remote agents, and context engineering capabilities. The demonstration showcases Augment's ability to build full-stack applications from scratch, debug complex issues, and automate repetitive tasks. He encourages viewers to try Augment's 14-day free trial to experience its benefits firsthand.