GitHub has announced that Stacked Pull Requests are now available in public preview, introducing native support for breaking large software changes into smaller, dependent pull requests that can be reviewed and merged independently. The feature aims to address one of the most persistent challenges in modern software development: balancing the increasing speed of code generation, particularly with AI-assisted development, against the limited capacity of human code reviewers.
Rather than submitting a single pull request containing hundreds or thousands of lines of code, developers can now organize work into a sequence of smaller pull requests, each building on the previous one. Reviewers can evaluate each logical change independently, reducing cognitive load while allowing development to continue without waiting for an entire feature to be completed before review begins. GitHub's implementation also preserves existing branch protection rules, review workflows, and merge policies, making stacked development a natural extension of existing repository practices rather than requiring a separate workflow.
The timing of the announcement reflects a broader shift across the software industry. AI coding assistants can now generate substantial amounts of production-ready code in minutes, but code review has remained largely a human activity. As development velocity increases, review quality risks becoming the primary bottleneck in software delivery.
Stacked pull requests attempt to solve this by reducing review scope rather than accelerating review itself. Instead of asking reviewers to understand dozens of unrelated changes simultaneously, each pull request represents a single logical step, such as introducing a new API, updating a database schema, or implementing a specific business capability. Smaller reviews are generally easier to understand, easier to validate, and less likely to introduce defects through overlooked changes.
Beyond improving reviews, stacked pull requests also support better developer flow. Large features often require foundational work that blocks subsequent development until reviews are completed. With stacked changes, developers can continue building on top of earlier work while reviewers evaluate each layer independently.
This enables more continuous collaboration between authors and reviewers while reducing the need for large, long-lived feature branches that frequently suffer from merge conflicts and stale code. It also encourages developers to think about software changes as incremental architectural improvements rather than monolithic feature deliveries, a practice already associated with lower deployment risk and faster feedback.
Although new to GitHub, stacked development is far from a new concept. Meta popularized "stacked diffs" through its internal development tooling, while companies such as Graphite and Sapling have built dedicated tooling around similar workflows. Many GitHub teams have previously relied on third-party tools or manual branch management to achieve comparable results.
By incorporating the capability directly into GitHub, Microsoft removes much of the operational overhead that previously discouraged widespread adoption. Teams can now create, review, update, and merge dependent pull requests using familiar GitHub workflows without introducing additional tooling into their engineering environments.
Recent research reinforces this challenge. An analysis of early adoption of agentic coding tools found that while AI-generated pull requests are becoming more common, successful integration still depends heavily on human oversight, with most projects relying on a single experienced reviewer to validate agent-generated contributions before merging. Other research examining practitioner opinions concluded that code review has become the primary control point determining whether AI improves or degrades software quality, making structured review processes more important as coding agents become more capable.
Companies including Graphite have built platforms dedicated to stacked development, while modern engineering organizations increasingly emphasize smaller pull requests, trunk-based development, continuous integration, and incremental deployment to reduce delivery risk.
The trend also aligns with recent innovations from GitHub itself, including Copilot coding agents, CodeQL enhancements, artifact attestations, secret scanning through MCP, and expanded AI-assisted development workflows. Together, these initiatives suggest that the future of software engineering will depend less on generating more code and more on creating workflows that allow humans and AI to collaborate safely, transparently, and at scale.
As AI continues to accelerate software development, features like stacked pull requests may become less a convenience and more a necessity. In an era where generating code is becoming increasingly automated, the ability to review, understand, and safely integrate that code may prove to be one of the defining capabilities of high-performing engineering organizations.