How to use an AI coding agent like Jules

The quick answer

You can start using an AI coding agent to automate development tasks. Here is the typical process:

1. Connect the AI coding agent to your private or public code repository.
2. Assign a complex task, like "write unit tests for this feature," using a natural language prompt.
3. Let the autonomous agent work in a secure, sandboxed environment in the background.
4. Review the completed code after the agent opens a pull request for your team.

What is an AI coding agent?

An AI coding agent is a tool that goes beyond simple code completion. Unlike assistants that suggest snippets, an agent can understand intent, plan multi-step tasks, and execute them independently.

Google's Jules is a leading example of this technology. It functions as an autonomous agent within your developer toolchain. It reads your entire codebase to understand context and then performs complex work on your behalf, acting like a true teammate.

These agents are powered by advanced models like Google's Gemini 2.5 Pro. This allows for deep code understanding and reasoning, enabling the tool to handle sophisticated requests that a simple code-completion tool cannot.

From copilot to autonomous agent

The key difference is autonomy. A copilot or code completion tool helps you write code faster line by line. An AI coding agent takes a high-level goal and figures out how to achieve it on its own.

You can assign a task, close your laptop, and come back later to find the work done. This asynchronous workflow is a fundamental shift from the real-time, synchronous nature of most coding assistants today.

How an AI coding agent works in your toolchain

Integrating an AI coding agent like Jules into your workflow is a direct process. It is designed to fit into existing developer toolchains with minimal disruption.

The agent securely connects to your repositories and operates within a controlled environment, giving you a powerful new way to manage your development lifecycle.

Step 1: Connect your repository

The first step is to grant the agent access to your codebase. Developers connect Jules to their repositories, including those on GitHub. The agent clones the code into a secure Google Cloud virtual machine.

This sandboxed environment ensures your source code remains isolated and secure. The agent works within this container to perform tasks, preventing any unintended direct effects on your main development branches until you approve them.

Step 2: Assign tasks asynchronously

Once connected, you assign work using natural language prompts. Instead of writing code, you write instructions. You can ask Jules to write new tests, build entire features, fix known bugs, or update project dependencies.

Because Jules operates asynchronously, you do not need to wait for it to finish. You can assign multiple tasks and let the agent work in the background. This frees up your developers to focus on higher-level architectural decisions and creative problem-solving.

Step 3: Review the completed work

When a task is complete, the agent delivers the result in a standard developer format. Jules can automatically open new branches and submit pull requests on GitHub for your team to review.

The system also uses Environment Snapshots to save dependencies and scripts. This ensures that tasks run faster and more consistently every time, reducing setup friction for future work.

Key features of a modern AI coding agent

When evaluating an AI coding agent, certain features separate a true autonomous tool from a simple assistant. These capabilities are what enable genuine workflow automation.

Look for tools that offer deep integration, security, and the ability to work independently. Just as an AI coding agent streamlines development, a fully managed digital marketing plan streamlines your growth online.

• Deep Code Understanding: The agent should be powered by an advanced AI model capable of understanding the context of an entire codebase, not just the current file.
• Autonomous Execution: It must be able to plan and execute multi-step tasks without constant user supervision.
• Secure Code Handling: Look for agents that use sandboxed environments, like virtual machines, to process your code securely.
• Asynchronous Workflow: A key feature is the ability to run tasks in the background, allowing you to delegate work and focus elsewhere.
• Full Toolchain Integration: The agent should integrate with your existing tools, like GitHub, and be able to perform actions like opening pull requests.

Practical use cases for your development team

An AI coding agent is not a theoretical tool. It provides practical solutions to time-consuming development tasks. Integrating an agent can immediately boost your team's productivity and code quality.

Building efficiently with an AI coding agent is the first step. The next is ensuring your final product is built to rank and designed to convert for maximum impact.

Use case 1: Automate unit and integration tests

Writing tests is critical but often tedious. You can delegate this entire task to an AI agent. Provide a prompt that specifies the file or feature needing test coverage.

Example Prompt: "Analyze the `user-authentication.js` file. Write comprehensive Jest tests to cover all exported functions. Include edge cases for invalid email formats, weak passwords, and token expiration. Aim for 100% test coverage and place the new test file in the `__tests__` directory."

The agent will read the file, write the tests, and submit a pull request with the new test suite ready for review.

Use case 2: Manage dependency updates

Keeping dependencies up to date is a constant security and maintenance chore. An agent can automate this process, identify breaking changes, and even attempt to fix them.

Example Prompt: "Update all npm packages in `package.json` to their latest stable versions. After updating, run the test suite. If any tests fail, identify the breaking changes and attempt to fix them. Document any unresolved issues in the pull request description."

Use case 3: Accelerate bug fixing

You can feed bug reports directly to an AI agent. By providing the error message, user report, and relevant code files, the agent can investigate, diagnose, and propose a fix.

Example Prompt: "There is a bug where users get a '500 Internal Server Error' when uploading a profile picture larger than 5MB. The relevant code is in `profileController.js`. Investigate the issue, write a fix that implements a 5MB file size limit with a clear error message for the user, and write a test to confirm the fix."

The competitive landscape and what it means for you

The market for AI coding assistants is heating up. Tools like GitHub Copilot pioneered code completion, but the new wave of agents like Jules, Cursor, and others focuses on autonomy.

Google is heavily invested in this space, with Jules leading the charge as an autonomous agent. This competition is good for developers, as it drives innovation, improves features, and creates more powerful options for automating workflows.

This trend toward autonomous agents shows that the future of software development involves delegating more complex, systematic tasks to AI. This allows human developers to act as architects and reviewers, not just builders.

Jules pricing tiers explained

After its beta period, Google Jules introduced a tiered pricing structure based on real-world usage data. This ensures developers can choose a plan that matches their workflow needs.

• Free Plan: Includes 15 daily tasks and 3 concurrent tasks. This plan is ideal for individual developers or those wanting to explore the agent's capabilities.
• Pro Plan: At $19.99/month, this plan offers 5 times the limits of the free plan, suited for professional developers who regularly delegate tasks.
• Ultra Plan: At $124.99/month, this provides 20 times the limits. It is designed for power users, freelancers, and small teams with advanced, high-volume workflows.

Your privacy and security with AI agents

Handing your codebase to a third-party tool naturally raises questions about privacy and security. Google has addressed this directly in response to user feedback, clarifying its data handling policies for Jules.

It is crucial to understand the terms before connecting any tool to your private code. You can review the official Google Labs Terms and Privacy for detailed information.

Public vs. private repositories

The policy is straightforward. For public repositories, the code may be used to help train the underlying AI models. This is a common practice for open-source code.

However, for private repositories, your code is not used for training. It is only used within the secure, sandboxed environment to complete the tasks you assign. This clear distinction is essential for any business using an AI agent for proprietary projects.

Once your development is optimized, ensure your full online presence is performing just as well. Our monthly plans cover everything from website to social, so you can focus on your product.

Your next steps with an AI coding agent

Adopting an AI coding agent can significantly improve your team's efficiency. The key is to start small and integrate the tool thoughtfully into your existing workflow.

Follow these steps to get started:

1. Evaluate Your Workflow: Identify time-consuming, repetitive tasks like writing boilerplate code, updating dependencies, or generating tests. These are perfect candidates for automation.
2. Start with a Free Tier: Sign up for the free plan of an agent like Jules. Use it on a non-critical side project to understand its capabilities and limitations without risk.
3. Practice Your Prompts: Learning to write clear, specific, and context-rich prompts is the most important skill for using an agent effectively. Treat it like communicating with a junior developer.
4. Define Team Guidelines: If you plan to use an agent across your team, create simple guidelines for when and how to use it. This ensures consistency in code quality and review processes.
5. Assess the Return on Investment: Track the time saved on tasks delegated to the agent. This data will help you justify moving to a paid plan when your needs grow.