The era of AI coding assistants has rapidly evolved. We started with simple autocomplete, graduated to chat-based "copilots" that could explain code or write functions, and now, we are entering the age of agents.
Google has officially thrown its hat into the agentic ring with Jules, an experimental, autonomous AI coding agent designed to handle complex development tasks asynchronously. Unlike the tools you might be using today that require you to tab-complete your way through a file, Jules is designed to take a ticket, understand your repo, and ship a pull request.
Here is everything you need to know about Jules and why it might just be the new favorite tool in your dev stack.
What is Jules?
Jules is an autonomous coding agent that integrates directly with GitHub. Instead of living in your IDE as a sidebar, Jules operates more like a remote contractor or a digital teammate.
It runs in a secure Google Cloud virtual machine, giving it a distinct advantage over local LLMs or simple text-generators: it can clone your repository, install dependencies, run build scripts, and verify its own changes before showing them to you.
How Jules Works: The "Fire and Forget" Workflow
The biggest shift Jules introduces is the asynchronous workflow. You don't sit and watch Jules type. You assign a task and move on to something else.
Here is the typical lifecycle of a Jules task:
Connect & Select: You log in with your Google account and grant Jules access to your GitHub repositories. You select the repo and branch you want to work on.
Prompting: You provide a natural language prompt. This can be as specific as "Fix the race condition in the
userAuthmodule" or as broad as "Add a test suite for the new API endpoints."The Plan: Before writing a single line of code, Jules analyzes your codebase and generates a Plan. This is a high-level overview of what it intends to do. You review this plan, and if it looks solid, you hit approve.
Execution: Jules spins up a VM, implements the changes, and (crucially) attempts to verify them.
Review: Once finished, Jules notifies you. You get a diff of the changes, which you can then refine or merge directly as a Pull Request.
Key Feature: The AGENTS.md File
One specific detail found in the documentation that developers should note is the support for an AGENTS.md file.
Jules looks for this file in the root of your repository. You can use it to document architectural patterns, tool usage, or specific conventions that an AI agent should know but might not infer from code alone. Think of it as a README.md, but specifically optimized to give context to your AI agents. This is a great way to "onboard" Jules to your specific codebase quirks.
Why This Matters for Developers
If you are a reader of flowdevs.io, you know we care about developer flow. Context switching is the enemy of productivity.
Current AI tools often increase context switching because they require constant supervision. You have to prompt, read, refine, and copy-paste. Jules aims to reduce this cognitive load. By handling the "grunt work" of dependency updates, boilerplate generation, or test writing in the background, it allows you to stay focused on high-level architecture or the complex problem you're currently solving.
Getting Started
Jules is currently in an experimental phase, but getting started is straightforward:
Head over to jules.google.
Sign in and link your GitHub account.
Pick a repo and describe a task.
Pro Tip: Try adding an
AGENTS.mdfile to your repo before starting to see if it improves the quality of the plan Jules generates.
The Verdict
Jules represents the next step in AI-assisted development. It moves us away from "AI as a fancy typewriter" toward "AI as a collaborator." While it is still experimental, the ability to offload entire tickets to an asynchronous agent is a glimpse into the future of software engineering.
Book some time with us and lets talk about how the future of coding is here: https://bookings.flowdevs.io
The era of AI coding assistants has rapidly evolved. We started with simple autocomplete, graduated to chat-based "copilots" that could explain code or write functions, and now, we are entering the age of agents.
Google has officially thrown its hat into the agentic ring with Jules, an experimental, autonomous AI coding agent designed to handle complex development tasks asynchronously. Unlike the tools you might be using today that require you to tab-complete your way through a file, Jules is designed to take a ticket, understand your repo, and ship a pull request.
Here is everything you need to know about Jules and why it might just be the new favorite tool in your dev stack.
What is Jules?
Jules is an autonomous coding agent that integrates directly with GitHub. Instead of living in your IDE as a sidebar, Jules operates more like a remote contractor or a digital teammate.
It runs in a secure Google Cloud virtual machine, giving it a distinct advantage over local LLMs or simple text-generators: it can clone your repository, install dependencies, run build scripts, and verify its own changes before showing them to you.
How Jules Works: The "Fire and Forget" Workflow
The biggest shift Jules introduces is the asynchronous workflow. You don't sit and watch Jules type. You assign a task and move on to something else.
Here is the typical lifecycle of a Jules task:
Connect & Select: You log in with your Google account and grant Jules access to your GitHub repositories. You select the repo and branch you want to work on.
Prompting: You provide a natural language prompt. This can be as specific as "Fix the race condition in the
userAuthmodule" or as broad as "Add a test suite for the new API endpoints."The Plan: Before writing a single line of code, Jules analyzes your codebase and generates a Plan. This is a high-level overview of what it intends to do. You review this plan, and if it looks solid, you hit approve.
Execution: Jules spins up a VM, implements the changes, and (crucially) attempts to verify them.
Review: Once finished, Jules notifies you. You get a diff of the changes, which you can then refine or merge directly as a Pull Request.
Key Feature: The AGENTS.md File
One specific detail found in the documentation that developers should note is the support for an AGENTS.md file.
Jules looks for this file in the root of your repository. You can use it to document architectural patterns, tool usage, or specific conventions that an AI agent should know but might not infer from code alone. Think of it as a README.md, but specifically optimized to give context to your AI agents. This is a great way to "onboard" Jules to your specific codebase quirks.
Why This Matters for Developers
If you are a reader of flowdevs.io, you know we care about developer flow. Context switching is the enemy of productivity.
Current AI tools often increase context switching because they require constant supervision. You have to prompt, read, refine, and copy-paste. Jules aims to reduce this cognitive load. By handling the "grunt work" of dependency updates, boilerplate generation, or test writing in the background, it allows you to stay focused on high-level architecture or the complex problem you're currently solving.
Getting Started
Jules is currently in an experimental phase, but getting started is straightforward:
Head over to jules.google.
Sign in and link your GitHub account.
Pick a repo and describe a task.
Pro Tip: Try adding an
AGENTS.mdfile to your repo before starting to see if it improves the quality of the plan Jules generates.
The Verdict
Jules represents the next step in AI-assisted development. It moves us away from "AI as a fancy typewriter" toward "AI as a collaborator." While it is still experimental, the ability to offload entire tickets to an asynchronous agent is a glimpse into the future of software engineering.
Book some time with us and lets talk about how the future of coding is here: https://bookings.flowdevs.io
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