Your SDLC needs a productivity context engine

What if the secret to fixing your overwhelmed SDLC is not a better AI coding model, but a smarter productivity context engine? This week on Dev Interrupted, LinearB founders Ori Keren and Dan Lines join the show to discuss the messy middle of AI adoption and the painful transition from the traditional SDLC to the Agentic Development Life Cycle. They unpack why the era of cheap AI experimentation is over, how rising token costs are forcing engineering leaders to prioritize strict business ROI, and how autonomous tools are fundamentally changing the daily workflow of developers. Register here: for the June 25th workshop, Life Beyond Tokenmaxxing, to learn how to measure real AI impact and ROI across the SDLC. Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Leave us a Review Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: LinearB: Learn how to transform your SDLC and build an engineering context engine at linearb.io gitStream: Explore LinearB's workflow automation tool for routing pull requests at linearb.io/platform/gitstream Follow Ori and Dan on LinkedIn: Ori Keren | Dan Lines OFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.