LinearB launches Agentic Studio to give enterprise engineering leaders and coding agents the context to ship better software faster

Engineering productivity platform LinearB’s latest research reports on coding boom

Boston, MA, Nov. 02, 2026 (GLOBE NEWSWIRE) -- LinearB, a Leader in the 2026 Gartner Magic Quadrant for Developer Productivity Insight Platforms, today launched Agentic Studio, a key part of the next generation of developer productivity platforms.

Historically, productivity platforms gave engineering leaders metrics. LinearB now turns everything happening across software delivery into context that people and agents can consume and act on. Engineering leaders ask questions in plain language and get answers and next steps. They define the context surface with role-based access built in, and AI assistants help extend that context and build metrics, artifacts and dashboards. Coding agents receive that context, so they understand their task and environment better and make smarter decisions.

In LinearB's Agentic Studio, the AI assistant builds a custom metric from a plain-language request.

Acting on that context matters more than ever. LinearB’s 2026 AI benchmarks, covering 2.7 million pull requests across more than 250 organizations, show that developers who use AI most heavily nearly doubled their merged pull requests year over year. Yet the software factory faces new problems. In LinearB’s 2026 Software Engineering Benchmarks Report, AI-assisted pull requests were 2.6 times larger than unassisted ones at the 75th percentile, and agent pull requests waited more than five times longer for review.

LinearB founders Ori Keren and Dan Lines. 

“Context is the new king,” said Ori Keren, CEO and co-founder of LinearB. “We realized we hold a knowledge graph that nobody else has of everything happening across the software delivery lifecycle, and that’s exactly what agents and teams need to deliver high quality software, faster. So we rebuilt LinearB around it.”

LinearB’s effort skill, now in early access, matches a coding agent’s effort to the task. It gauges a change’s business relevance, complexity and risk using the code quality in the area, recent service incidents and ownership history. Low risk changes use less effort and fewer tokens. Complex, risky or high priority changes get more effort and careful, tested edits. More skills are on the way, focused on accelerating downstream stages of the software factory, such as review and merge.

In LinearB's Agentic Studio, the AI assistant traces a cycle time spike to review delays and flags agent-opened PRs that have waited longest.

Kraken used LinearB’s MCP server with AI to find what was really slowing its code reviews. The answer wasn’t large changes. It was time zones and reviewer availability. Kraken cut review time at the 90th percentile by more than half in four months. “That’s the structural shift,” said Nik Sudan, engineering operations lead at Kraken. “It moves leaders from anecdote-driven to evidence-driven.”

The Agentic Studio is available now to all LinearB customers. Agent skills are available in early access.

Why it matters now
Engineering leaders have more data than ever and less time to use it. Boards and CFOs now ask whether AI spend is paying off, and a dashboard full of numbers doesn’t answer that. At the same time, coding agents write more of the code, and they start every task knowing nothing about the codebase’s history.

Productivity platforms have always been good at measuring. The next step is turning what they measure into action. That means answers for the people running engineering, and context for the agents doing more of the work.

What LinearB does
LinearB collects data from across the software delivery lifecycle and makes it usable. It normalizes data from different Git and project management systems into one model, matches each engineer’s identities across tools, and links every change from ticket to release. It adds AI attribution and industry benchmarks, so leaders can tell whether a number is good or bad.

The engineering context layer delivers that knowledge to people and to agents.

Engineering leaders ask questions in plain language, like where delivery is stuck, how their developers are doing and whether AI is paying off. Instead of reading a chart, they get an answer and a next step. They can build their own metrics and pull the same context into AI tools like Claude through MCP.

Coding agents get the same context through MCP and agent skills. The first skill, now in early access, matches an agent’s effort to the task. It checks how complex and risky a change is, using the code’s rework rate, recent incidents and ownership history. Simple, low risk changes use less effort and fewer tokens. Complex or risky changes get more effort and careful, tested edits, which raises quality where it matters most. The skill works with any coding agent, and leaders can see how often agents were routed to low, medium and high effort.

The platform is built for the enterprise, with role based access and guardrails on what agents can do.

Journey and milestones
LinearB started by giving engineering leaders visibility into how their teams deliver software. Years of integration work across the software delivery lifecycle gave it one of the largest datasets on how engineering teams work. Its 2026 Software Engineering Benchmarks Report drew on more than 8.1 million pull requests from 4,800 engineering teams in 42 countries. As AI coding tools spread, LinearB added AI attribution and connected to more of the tools teams use.

In 2026, Gartner named LinearB a Leader in its first Magic Quadrant for Developer Productivity Insight Platforms, for both its ability to execute and its completeness of vision. Early customer response to the context layer is strong. The AI assistant and MCP access show the highest user retention of any features LinearB has released.

“These are exciting times to rethink our role in the software ecosystem,” said Ori Keren. “Everyone else is focused on writing code. We realized we hold a knowledge graph of everything happening across the software delivery lifecycle, and that’s exactly what developers and agents need. So we rebuilt LinearB around it. We don’t build the software factory. We instrument it, so leaders know what to do next and agents make smarter decisions on every change.”

“For years, engineering leaders told us the same thing. Metrics are useful, but they don’t tell you what to do,” added Dan Lines, co-founder and COO of LinearB. “A dashboard says your cycle time is two days. So what? Now a leader can ask what to improve in their engineering organization and get a clear answer. And when that same context goes to coding agents, the platform isn’t just reporting anymore. It’s acting.”

Market trends
LinearB’s 2026 AI benchmarks, built from 2.7 million pull requests and 83,000 developers across 253 engineering organizations between February and May 2026, show why measuring AI isn’t enough on its own. The teams pulling ahead are the ones acting on what they learn.

• Heavy AI users are pulling away. The typical developer who uses AI on at least 75% of coding days merged 96% more pull requests per week than a year earlier. The typical developer using no AI merged 3.6% fewer.
• Adoption is shallower than the hype suggests. Even at elite organizations, AI assists only 54% of pull requests.
• Agents still need people. At the 90th percentile, autonomous agents open 4.7% of pull requests. At elite organizations, 79% of agent pull requests merge within 30 days, compared with 92% of human only pull requests. At fair organizations, agent yield drops to 37%. The likely reason is ownership. When no developer owns an agent’s pull request, it often never merges.
• AI code review is the fastest win. Pull requests with an AI review merge at the highest rate of any type and lift yield by up to five percentage points.
• AI costs are climbing fast at the top. Token spend at the 90th percentile reached $35.20 per developer per coding day, up nearly 75% in three months, while the median barely moved.

Engineering leaders already use LinearB to answer the questions their boards are asking about AI. “I use LinearB data and charts directly in my board decks, because my board is specifically asking questions about our use of AI and what our ROI is,” said Ken Ringdahl, CTO of Emburse.

Forward-looking vision
The effort skill is the first in a library of agent skills. Each one turns LinearB’s context into an action an agent or a leader can take. Next, LinearB will focus on helping teams merge and govern AI generated code safely, and on cutting what engineering organizations spend on AI. The goal is the one productivity platforms have always promised, helping teams deliver better software faster. LinearB now does it by giving people answers and giving agents the context to act.

Media images can be found here. 

About LinearB
LinearB is the developer productivity platform built on a foundational engineering context layer. It turns data from the tools teams already use into context for agents, developers and engineering leaders. Teams use it to find bottlenecks, prove what AI delivers and guide agents toward safer, cost-aware changes. Learn more at https://linearb.io/

For further information please contact Kiersten Gaffney at LinearB on kiersten@linearb.io