LinearB Launches Agentic Studio as AI Coding Growth Tests Software Delivery
LinearB launched Agentic Studio in Boston on November 2, bringing software delivery data into a conversational workspace for engineering leaders and AI coding agents. The developer productivity company is targeting a growing gap between the speed at which teams generate code and their ability to review, integrate and release it.The product combines information from development tools with records…
LinearB introduced Agentic Studio in Boston on November 2, integrating software delivery metrics into a conversational interface for engineering leaders and AI coding tools. The aim is to address the gap between code generation speed and review/merge capabilities. This platform unites development tool data with code ownership and incident records, allowing managers to inquire about bottlenecks and AI investment using everyday language.
AI coding agents leverage this context to optimize their tasks. Currently in early access, Agentic Studio's features, such as adjusting agent effort levels, are still under development. Pricing and performance metrics will be released later. The company's research draws from two datasets: one from 2026 AI benchmarks (2.7 million pull requests from 83,000 developers in 253 organizations) and another broader report (8.1 million pull requests from 4,800 teams in 42 countries).
Developers using AI on 75% of coding days merged 96% more pull requests weekly compared to a year ago, while those with no AI merged 3.6% fewer. However, AI-assisted pull requests were significantly larger (2.6 times) and waited five times longer for review than unassisted ones. This highlights the need for effective review processes as AI speeds up coding.
LinearB collects data from Git repositories and project management systems, linking changes across tools and assigning AI attribution. Agentic Studio's assistant answers queries with charts, figures, and data context, enabling users to track delivery efficiency, AI adoption effects, and effort distribution. Currently, only certain applications can access LinearB's engineering data through the Model Context Protocol.
The CEO highlighted AI's role in rebuilding the platform around engineering needs and team requirements. Initial skills assess business relevance, complexity, and risk of proposed changes, applying effort differently based on change severity. While costs have risen, the company plans to address review and merge capacities to maximize AI's benefits.
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