LinearB launches Agentic Studio to give enterprise engineering leaders and coding agents the context to ship better software faster. November 2, 2026 by BPM Team Boston, Massachusetts - November 2, 2026; LinearB, a Leader in the 2026 Gartner Magic Quadrant for Developer Productivity Insight Platforms, today launched the agentic studio, a key part of the next generation of the 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. 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. "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. 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. 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/

LinearB launches Agentic Studio to help engineering leaders and AI coding agents build better software faster. November 2, 2026 LinearB, a Leader in the 2026 Gartner Magic Quadrant for Developer Productivity Insight Platforms, today launched the agentic studio, a key part of the next generation of the 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. 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. "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. 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. 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/

World's top crypto adopter Brazil closes license deadline. 02 November 2026, 03:36 UTC Updated 02 November 2026, 03:36 UTC * Brazil's deadline for crypto firms to seek a central bank license ended on October 30, with 68 institutions identified in the race. * Brazil, ranked first in Chainalysis' 2026 crypto adoption index, will now have banks such as Itaú, Bradesco and BTG competing with Binance, Coinbase and Kraken. * Crypto firms that missed the deadline must shut down their services in Brazil within 30 days. Trade without KYC & get bonus up to $2888. Explore BYDFi Brazil's largest banks, its stock exchange and some of the world's biggest crypto exchanges are among the companies seeking a Brazil crypto license from the Central Bank of Brazil (BCB). They aim to operate as virtual asset service providers in the country. The deadline for firms already active in the market ended on Friday, October 30. The stakes are high. Brazil ranked first in Chainalysis' 2026 Global Crypto Adoption Index, ahead of the United States and Nigeria, among 117 countries. Why Brazil's crypto market is too big to ignore. The country recorded $252.5 billion in crypto activity between July 2025 and June 2026. The largest crypto economy in Latin America by a wide margin. Nearly half of all crypto value moving through the region now passes through Brazil. By 7:30 p.m. local time on Friday, 68 institutions had been identified in the race. This is according to a tracker run by Cainvest Group, a Brazilian financial infrastructure firm that monitors the filings. The list includes requests confirmed by the companies themselves, regulatory notices filed by banks that already hold central bank authorization, and cases identified only through documents linked to BCB proceedings, not yet confirmed by the companies. Among the proceedings with public status, three were on the central bank's decision agenda. Two were under review and two were awaiting documents or responses from the applicants. Who Is in the Race for a BCB License? Banks, exchanges and stablecoin firms join the licensing race. Among banks, the stock exchange and other financial institutions are B3, Brazil's main stock exchange. They are there along with Itaú Unibanco, Bradesco, Nubank, Banco Inter, BTG Pactual (through its crypto unit Mynt), Banco BS2, Banco Rendimento, Braza Bank, Stark Bank, S3 CACEIS and B&T Corretora de Câmbio. The exchanges and brokers include Binance, Coinbase, OKX, Bybit, Kraken, Bitget, Ripio, OSL Brasil, Webull, Coins.xyz and Wynx Exchange. As well as Brazilian platforms Mercado Bitcoin, Foxbit and Brasil Bitcoin. Stablecoin, payments, and infrastructure companies also joined the race. These include Ripple, Avenia, Bloquo, Liqi, Lumx, Transfero, RedotPay, Parfin, NoxPay, Obsidiam, Belo, TCR Finance, Velaro Network and Manteca. The latter operates in Brazil as Ponte Global. Rounding out the list are BlockRiver, 4Pay Finance, ARQ Finance, Axioma, BCX Corretora, Bloxtrade Capital, Cademartori Z Soluções, Crown, Ettica Pagamentos, Frontrade, GGPay, IACrypto, MadarBit, Masterpay, Mazzera, NBS, Neo Ativos Virtuais, Nexus, Nonco, O3 Trade, Onda Finance, PFPay, Prata Digital, SuitCoin, TRKBIT, UnblockPay, Urban and W Brasil Comércio. Big banks join the race. Itaú and Bradesco, two of Brazil's largest private banks, are among the traditional lenders that formalized their virtual asset activities. They are there alongside digital banks Nubank and Inter and investment bank BTG Pactual. Because these institutions already hold central bank authorization, most of them followed a simpler regulatory notification process rather than applying for a new license from scratch. B3 also entered the race, applying to provide virtual asset intermediation and custody services. The move is part of the exchange's broader push into tokenized financial products. The presence of traditional institutions shows that Brazil's regulated crypto market will not be left to crypto-native firms alone. In the world's top market for crypto adoption, banks will be able to compete directly with exchanges in services such as crypto trading and custody. Global and Brazilian exchanges. The list brings together nearly every major global exchange with a presence in Brazil, as well as local leaders such as Mercado Bitcoin, one of the largest crypto platforms in Latin America. Coinbase confirmed its application on October 29, one day before the deadline. Not every platform decided to stay. Revolut has ended crypto sales to Brazilian customers and will automatically convert any remaining balances into US dollars after November 24. What changes now? Brazil's crypto rules were set out in BCB Resolutions 519, 520 and 521, published in November 2025 and in force since February 2, 2026. The rules implement the country's Virtual Assets Legal Framework (Law 14,478/2022) and created a new category of regulated entity: virtual asset service provider companies, known locally as SPSAVs. Under the framework, firms can be authorized as virtual asset intermediaries, custodians or brokers. Companies already operating when the rules took effect were given 270 days to file the first phase of their authorization request. Those that met the deadline can keep operating while the BCB reviews their applications. Those that did not must stop providing services within 30 days. As of October 30, BCB Resolution 520 also bars banks, payment institutions and other central bank-authorized entities from executing, intermediating or holding in custody any transactions involving virtual asset providers that are neither authorized nor in the process of authorization. In practice, crypto firms that missed the deadline will be cut off from Brazil's banking system. Filing a request, however, is only the first step. The application opens a two-phase review, and companies will still need to meet additional regulatory requirements before receiving final authorization. Daily Crypto Insights Insights, news and analysis of the crypto market straight to your inbox More on Regulation News? Disclaimer BeInCrypto is committed to unbiased, transparent reporting. This news article aims to provide accurate, timely information. However, readers are advised to verify facts independently and consult with a professional before making any decisions based on this content. Please note that our Terms and Conditions, Privacy Policy, and Disclaimers have been updated.