June 2026 Summaries

Igor Sechyn, an engineer at Linear, shares his experiences in developing and refining the Linear Agent, an AI tool designed to autonomously fix bugs and assist in software development. Initially skeptical of AI's capabilities, Sechyn's perspective evolved as he observed the technology's strengths and limitations, particularly in automating tasks like bug fixes within a software environment. The Linear Agent operates by reading and writing code, taking actions based on pre-set rules, and requires careful context and instructions to function effectively. It excels at addressing specific, well-defined issues such as removing dead feature flags, but struggles with more variable tasks like fixing failing background processes, landing a correct fix only about a third of the time. Sechyn emphasizes the importance of clear coding conventions and structured workflows to enhance the agent's efficiency, noting that the agent's integration into the team’s workflow transforms the development process into a collaborative effort. Through techniques like task-splitting and creating automations, Sechyn highlights how the agent complements human engineers by handling routine tasks, allowing them to focus on more complex challenges, thus redefining the engineering role rather than replacing it.

Linear has introduced new features to integrate AI agents into the software development process, making it a collaborative team effort rather than an individual task. The Linear Agent can now conduct coding sessions, automate triage, and manage code reviews, all within the same system, to streamline the transition from issue identification to implementation. By utilizing Code Intelligence and connecting with other tools like Sentry and Datadog, the agent can understand issues, propose solutions, and write code, which can then be reviewed and merged directly in Linear. This integrated workflow allows teams to maintain a continuous loop of context and collaboration, enabling them to efficiently handle tasks and iterate on solutions without losing track of the initial problem or discussion. Companies like Ramp and Coinbase have successfully implemented these workflows, demonstrating how Linear can serve as a central context layer for product development.

In June 2026, Linear launched "Diffs," a feature designed to streamline the pull request (PR) review process, in response to an increase in code volume due to advancements in AI models and agents. This feature allows users to review code directly within Linear, making it easier to manage the surge of reviews without compromising quality. The tool provides a structured review environment by organizing changes into manageable chunks and integrating notifications to ensure timely feedback. It emphasizes understanding the rationale behind code changes, focusing on product sensibilities, user experience, and real-world impact, rather than just code correctness. This shift in focus enhances the role of engineers, encouraging a deeper engagement with the product's purpose and fostering collaboration within the team. As a result, even with increased PRs, Linear maintains high code quality, ensuring that shipped code is not only correct but also valuable to the product, reflecting a mature engineering culture that prioritizes accountability and meaningful contributions.