Google’s AI coding agent Jules is now out of beta

Google on Wednesday launched its AI coding agent, Jules, out of beta, just over two months after its public preview debut in May. Powered by Gemini 2.5 Pro, Jules is an asynchronous, agent-based coding tool that integrates with GitHub, clones codebases into Google Cloud virtual machines, and uses AI to fix or update code while developers focus on other tasks. Google initially announced Jules as a Google Labs project in December and made it available to beta testers through a public preview at its I/O developer conference. Kathy Korevec, director of product at Google Labs, told TechCrunch that the tool’s improved stability drove the decision to take it out of beta after receiving hundreds of UI and quality updates during its beta phase.

“The trajectory of where we’re going gives us a lot of confidence that Jules is around and going to be around for the long haul,” she said.

New pricing tiers and usage limits

With the wider rollout, Google introduced structured pricing tiers for Jules, starting with an “introductory access” free plan capped at 15 individual daily tasks and three concurrent ones, down from the 60-task limit during beta. Jules’ paid tiers are part of the Google AI Pro and Ultra plans, priced at $19.99 and $124.99 a month, offering subscribers 5× and 20× higher limits, respectively. Korevec noted that Jules’ packaging and pricing are based on “real usage” insights gathered over the past couple of months.

“The 60-task cap helped us study how developers use Jules and gave us the information we needed to design the new packaging,” Korevec explained. “The 15/day is designed to give people a sense of whether Jules will work for them on real project tasks.”

Google also updated Jules’ privacy policy to clarify how it trains AI. Public repositories may be used for training, but private repositories are not.

“We got a little bit of feedback from users that it [the privacy policy] wasn’t as clear as we thought it was, and so most of it is just responding to that. We didn’t change anything about what we’re doing on the training side, but we changed the language,” Korevec explained.

Beta feedback and new capabilities

During the beta, thousands of developers tackled tens of thousands of tasks, resulting in over 140,000 code improvements shared publicly. Initial feedback led the Google Labs team to add new capabilities, including reusing previous setups for faster task execution, integrating with GitHub issues, and supporting multimodal input. The two primary users of Jules so far are AI enthusiasts and professional developers. By running asynchronously in a virtual machine, Jules stands apart from top AI coding tools like Cursor, Windsurf, and Lovable, which all work synchronously and require users to watch the output after each prompt.

“Jules operates like an extra set of hands … you can basically kick off tasks to it, and then you could close your computer and walk away from it if you want and then come back hours later. Jules would have those tasks done for you, versus if you were doing that with a local agent or using a synchronous agent, you would be bound to that session,” Korevec explained.

This week, Jules received a deeper integration with GitHub to open pull requests automatically — just like it could open branches — and a feature called Environment Snapshots to save dependencies and install scripts as a snapshot for faster, more consistent task execution.

Insights from beta trials and mobile use

Since entering public beta, Jules has logged 2.28 million visits worldwide, 45% of them from mobile devices, according to data from market intelligence provider SimilarWeb, reviewed by TechCrunch. India was the top market for traffic, followed by the U.S. and Vietnam. Google did not share specifics on Jules’ user base and its top geographies. Korevec told TechCrunch that during the beta, many users leveraged Jules to fix bugs or extend vibe-coded projects to make them more production-ready. Originally, Jules required users to have an existing codebase. But Google soon enabled Jules to work even with an empty repository, increasing its scope and usage. The team also noticed an increasing number of users accessing Jules through mobile devices. Although there is no dedicated mobile app, users access it via the web app.

“Since it’s a big use case that we’re seeing emerging, we’re absolutely exploring what the features are that people need on mobile a lot more,” Korevec noted.

Alongside beta testers, Google already uses Jules internally to help develop some projects, with a “big push” underway to use the tool on many more projects at the company.

Frequently Asked Questions (FAQ)

What is Google’s Jules AI coding agent?

Jules is an asynchronous, agent-based AI coding tool developed by Google, powered by Gemini 2.5 Pro. It integrates with GitHub, clones codebases, and uses AI to fix or update code, allowing developers to focus on other tasks.

What are the pricing tiers for Jules?

Jules offers a free "introductory access" plan with daily task limits. Paid tiers are available as part of Google AI Pro and Ultra plans, offering significantly higher task limits at $19.99 and $124.99 per month, respectively.

How does Jules handle codebases?

Jules integrates with GitHub, clones codebases into Google Cloud virtual machines, and then uses AI to perform code improvements or updates.

What is the advantage of Jules being an asynchronous agent?

As an asynchronous agent, Jules can operate in the background without requiring constant user attention. Developers can initiate tasks and then step away, returning later to find the tasks completed, unlike synchronous agents that require users to monitor their output.

How does Google use Jules?

Google uses Jules internally for developing some of its projects and is actively working to expand its use across more projects within the company.

What kind of feedback did Google receive during the beta phase?

During the beta, Google received feedback that led to hundreds of UI and quality updates. This feedback also influenced improvements like reusing previous setups, integrating with GitHub issues, and supporting multimodal input.

How does Jules handle privacy and AI training data?

Google's privacy policy clarifies that public repositories may be used for training AI models, but private repositories are not. Google has updated its language to make this clearer based on user feedback.

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Google's AI coding agent Jules is now out of beta