If you could hire the smartest engineers and drop them in your code base would you expect miracles overnight? No, of course not! Because even if they are the best of coders, they don’t have context on your project, engineering processes and culture, security and compliance rules, user personas, business priorities, etc. The same is true of the very best agents.. they may know how to write (mostly) technically correct code, and have the context of your source code, but they’re still missing tons of context. Building agents that can deliver high quality outcomes, faster, is going to require much more than your source code, rules and a few prompts. Agents need the same full lifecyle context your engineers gain after being months and years on the job. LLMs will never have access to your company’s engineering systems to train on, so something has to bridge the knowledge gap and it shouldn’t be you, one prompt at a time. This is why we're building what we call our Knowledge Graph at GitLab. It's not just indexing files and code; it's mapping the relationships across your entire development environment. When an agent understands that a particular code block contains three security vulnerabilities, impacts two downstream services, and connects to a broader epic about performance improvements, it can make smarter recommendations and changes than just technically correct code. This kind of contextual reasoning is what separates valuable AI agents from expensive, slow, LLM driven search tools. We're moving toward a world where institutional knowledge becomes portable and queryable. The context of a veteran engineer who knows "why we built it this way" or "what happened last time we tried this approach" can now be captured, connected, and made available to both human teammates and AI agents. See the awesome demos below and I look forward to sharing more later this month in our 18.4 beta update!

I'm extremely excited to announce that Siemens and AIT Austrian Institute of Technology—two leaders in industrial innovation—chose TDengine as the time-series backbone for a groundbreaking project at TCG Unitech GmbH! Here’s the magic: Imagine stitching together over a thousand time-series signals per machine with domain knowledge, and connecting it all through an intelligent semantic layer. With TDengine capturing high-frequency sensor data, PostgreSQL holding production context, and Ontopic virtualizing everything into a cohesive knowledge graph—this isn’t just data collection. It’s an orchestration that reveals hidden patterns, powers real-time anomaly and defect detection, supports traceability, and enables explainable root-cause analysis. And none of this works without good semantics. The system understands the relationships—between sensors, machines, processes, and defects—which means both AI and humans can ask the right questions and get meaningful, actionable answers. For me, this is the future of smart manufacturing: when data, infrastructure, and domain expertise come together, you get proactive, explainable, and scalable insights that keep factories running at peak performance. It's a true pleasure working with Stefan B. from Siemens AG Österreich, Stephan Strommer and David Gruber from AIT, Peter Hopfgartner from Ontopic and our friends Klaus Neubauer, Herbert Kerbl, Bernhard Schmiedinger from TCG on this technical blog! We hope this will bring some good insights into how time-series data and semantics can transform the operations of modern manufacturing! Read the full case study: https://lnkd.in/gtuf8KzU