According to a report by Mike Wheatley on SiliconANGLE, application development intelligence startup LinearB Inc. announced today the launch of Agentic Studio to ease the growing congestion in enterprise software delivery pipelines that has arisen following the widespread adoption of artificial intelligence coding tools. The new product focuses on providing actionable context to both human software engineers and autonomous coding agents.
Rise in AI Coding Tools and the Creation of Bottlenecks
The company explains that the emergence of generative artificial intelligence tools for writing code, such as Claude Code and Codex, has created new headaches and difficulties for enterprise software development teams. Although writing software code may now be almost effortless, shipping that code into production has remained as painful a process as it has always been, and teams are now required to ship a much larger volume of code.
In its recent Software Engineering Benchmarks report, the company analyzed more than 2.7 million pull requests across 250 different organizations. The report found that organizations relying on AI coding tools nearly doubled their merged code changes year-over-year. However, the central problem is that AI-assisted pull requests are nearly 2.6 times larger compared to others, and consequently wait more than five times longer to undergo code review.
This situation has caused significant backlogs to accumulate. This difficulty is further compounded by the fact that code changes proposed by AI agents are merged just 79% of the time among organizations defined as "elite organizations," compared to a 92% merge rate when it comes to code written by human engineers.
Limitations of Traditional Dashboards and the Unified Context Layer
According to LinearB, human software engineers struggle to clear this accumulating backlog because the traditional dashboards they are forced to work with display metrics only, rather than providing the context required to resolve those pull requests. Through Agentic Studio, the company aims to address this problem by normalizing the data and insights generated by Git repositories and project tracking tools into a unified context layer.
This means that instead of software engineers having to puzzle and dig through various charts and graphs in an attempt to decipher why release velocity has stalled, they can ask direct questions in plain language to locate the root cause of the delay and receive actionable recommendations on how to speed up processes across the organization.
The Enterprise Knowledge Graph and Model Context Protocol
LinearB co-founder and Chief Executive Officer Ori Keren noted that context has become the central and most important factor, not only for AI tools but also for humans. Keren stated: "We realized we hold a knowledge graph that nobody else has of everything that is happening across the software delivery lifecycle. That's exactly what agents and teams need to deliver high quality software faster, so we rebuilt LinearB around it."
According to Keren, what makes Agentic Studio different is that the system was designed to help autonomous coding agents just as much as it helps human software engineers. Through integration via protocols such as the Model Context Protocol (MCP protocol), the system feeds AI agents with vital historical context, such as rework rates, past production incidents, and code ownership across different sections, thereby preventing the agents from making blind and groundless modifications to pull requests.
Practical Implementation at Kraken Exchange and Moving from Metrics to Action
According to the report, early adopters of the technology experienced a dramatic increase in productivity. An example of this is the cryptocurrency exchange Kraken, which used LinearB's MCP integration to pinpoint exactly why its code reviews were delayed. The exchange discovered that timezone misalignment across different regions was the primary culprit behind the delays, rather than pull request sizes, and subsequently reorganized its workflows, cutting code review times by more than half within just four months.
LinearB Chief Operating Officer (COO) Dan Lines stated: "For years, engineering leaders told us the same thing: metrics are useful, but they don't tell you what to do. A dashboard says your cycle time is two days, but so what? Now [with Agentic Studio] a leader can ask what to improve in their engineering organization and get a clear answer. When that same context goes to coding agents, the platform is not just reporting, it's acting."
Development of Agent Skills and Calibrating Computational Effort
To help AI agents turn the provided context into concrete actions, LinearB is developing a series of new agent skills. The first skill, designated as an "effort skill," is available now in early access and analyzes the complexity level of a pull request and its proposed code changes, alongside rework history and risk level, to dynamically calibrate how much computational effort a coding agent needs to expend during its work.
Through this mechanism, simple, low-risk changes are carried out faster and with minimal token usage. Conversely, more sensitive, high-priority modifications will be implemented using deeper reasoning capabilities and more exhaustive testing to ensure nothing goes wrong. The company noted that it plans to expand its agent skills library in the coming months, with additional skills focused on automated code review, merge governance, and token cost optimization.