PrAtlas - GitKraken Introduces Code Flow, a Framework for Software Development in the Agentic Era

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New research reveals developers are rapidly adopting AI agents, creating new challenges around coordination, control, and shipped software.

SCOTTSDALE, Ariz. - PrAtlas -- GitKraken today introduced a framework for understanding how software development is evolving as AI agents become a core part of the development process, a concept called Code Flow.

The announcement is accompanied by new research conducted by GitKraken in May 2026 among more than 550 software developers, revealing that AI adoption has moved well beyond experimentation.

Key findings include:

96% of respondents reported some level of AI adoption on their team
65% said more than half of their team has adopted AI tools
30% actively run multiple AI agents in parallel, with another 33% experimenting with this practice
28.5% are already allowing AI to operate autonomously, either individually or alongside other agents

"What's changing isn't just how developers write code - it's how they work," said Matt Johnston, CEO of GitKraken. "Increasingly developers are increasingly managing multiple agents on ever growing swaths of work, coordinating work across repositories, and evaluating AI-generated output. The challenge is no longer generating code. It's how to turn agent-generated code into high-quality, shipped software – from plan to main "

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As AI adoption accelerates, developers are beginning to work in ways that were unthinkable just 6-12 months ago. Instead of implementing a feature themselves, they may direct multiple agents working in parallel, use different models for different tasks, and spend more time reviewing and coordinating work than producing it directly.

GitKraken's research found that the top uses of AI in software development today are:

Generating and refactoring code
Troubleshooting and debugging
Conducting code reviews

These changes are creating a new set of challenges for engineering teams:

Which models are best suited for different types of work?
How many agents should be running simultaneously?
How prepared is a repository for agent-driven development?
How do teams maintain standards and context across parallel workstreams?
How do organizations safely integrate and merge dramatically larger volumes of code?

GitKraken customers coined the term for this emerging challenge as Code Flow.

Code Flow describes how work moves between developers, coding agents, repositories, planning systems, reviews, branches, and production environments. It encompasses the coordination, visibility, governance, and integration required to transform AI-generated output into working software.

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Historically, software development tools were built around a simple assumption: humans wrote code and Git tracked the result.

Today, developers may have multiple agents operating simultaneously across multiple repositories, creating significantly more branches, pull requests, commits, and merge activity than teams managed only a few years ago.

"The industry has spent years focused on helping developers write code faster," Johnston said. "As AI adoption grows, organizations need better ways to understand what's happening across their development systems, where work is getting stuck and how agents are performing. The challenge has become managing the exploding velocity and volume of work coding agents can create. That's what Code Flow is about."

GitKraken recognizes Code Flow will become an increasingly important discipline as organizations expand their use of AI agents and autonomous development systems – and demand positive returns from their investments in AI developer tooling.

Because in the agentic era, writing code is only part of the job.

Moving that code from plan to main, that is Code Flow.

About GitKraken

GitKraken is the Code Flow company. As AI transforms how software is created, GitKraken helps developers, teams, and organizations manage the flow of work between ideas and production. Trusted by millions of developers worldwide, GitKraken builds tools that improve coordination between developers, AI agents, repositories, and delivery systems—helping organizations turn software velocity into meaningful outcomes.