Jul 14, 2026 AI Engineer World’s Fair 2026: The Runtime Is Where Agent Trust Is Won We spent the week at AI Engineer World’s Fair in San Francisco, on stage and on the floor. Here’s what we heard, and where we think it lands for anyone building with agents. Aditya Tripathi and Srini Sekaran Read now
Author: drweb
I’m honored to announce that I’ve been renewed as a Microsoft MVP for the tenth consecutive year, recognized in the Azure SQL and SQL Server technical areas under Data Platform. Ten years. I honestly didn’t see that coming when I set a five-year goal back in 2016.Ten YearsI want to stop and acknowledge that milestone for a moment. When I first earned this award in 2017, I was deep in Availability Groups and SQL Server internals. Since then, the journey has taken me through SQL Server on Linux, containers, Kubernetes, storage integrations, and now AI-integrated SQL Server 2025. The platform…
Say you need outside help. You’ve exhausted all the things that you could do and, still, nothing works. So, you decide that calling in for help is the next sensible thing to do. Let’s say this is the first time you’re asking for somebody’s help. You decide which consultant to work with. The hardest part, if you’re doing this for the first time, is how to start that conversation.The first conversation is the most critical one. Obviously, you cannot just turn over your SQL Server instance and wait for the bill. Some prep changes what you get out of that…
I burned through half of my E2B sandbox budget in just two weeks by spinning up containers for AI agents that often ran for only eight seconds. I had been integrating AI coding agents into a few automation scripts on TecMint’s backend. These agents would generate a draft, run a command, check the output, and repeat the process until the task was complete. Since I didn’t want an AI agent running random shell commands directly on my production server, every task needed its own isolated environment. Like many people, I started with Docker and later switched to a hosted sandbox…
TL;DR — Key TakeawaysAnaconda’s acquisition of Kilo Code moves the company beyond Python tooling and into the AI coding-agent layer. Kilo gives Anaconda direct access to the developer interface where models are selected, agents are directed and organizational data is routed.The deal forms part of a broader platform strategy. Anaconda’s package and environment management, Outerbounds’ workflow orchestration and Kilo’s agentic development tools could become an end-to-end enterprise AI development platform.The opportunity is significant, but integration and developer trust will be critical. Anaconda must provide enterprise governance, security and visibility without undermining Kilo’s open-source flexibility, model neutrality or developer experience.The Kilo…
TL;DR — Key TakeawaysJira wants to run the agentic workflow. Atlassian is turning Jira into the place where AI work gets assigned, tracked, reviewed and governed.Coding agents are moving into Jira. Teams can connect Claude Code, Cursor, GitHub Copilot and Codex directly to development workflows.Jira Coding Agent can turn tickets into pull requests. Developers hand off the task, then review the result.Atlassian today extended the scope of tasks that artificial intelligence (AI) can automate directly from its Jira project management software, including assigning work to an AI coding agent.Initially, Jira integration with AI coding tools includes Claude Code from Anthropic,…
Every check is green. Every validation rule passed. The pipeline reports success in cheerful little checkmarks. And the dashboard is still, somehow, wrong. That gap is exactly what data pipeline monitoring exists to close, and most teams do not have it.This confused me for an entire afternoon once. Every rule I had written was passing, because every rule asked about the contents of a row, and every row was fine. What none of them could tell me was that yesterday’s file had never arrived. The table was full of perfectly valid data that was exactly one day stale.My checks were…
AI has moved very quickly from experimentation to production. A few years ago, many organizations were still asking whether AI could improve their products or internal workflows. Today, the question is different: how can teams ship AI-enabled software safely, reliably, and responsibly?That shift matters because AI is no longer just a research project or a boardroom talking point. It is being added to customer support platforms, fraud detection systems, developer tools, compliance workflows, cloud operations, marketing engines, and enterprise applications. The opportunity is real, but so is the risk.Traditional software usually behaves in predictable ways. If the logic is written…
The slides for my session “The €100 data warehouse on the Azure data platform” can be found on GitHub. It was a calm event, probably because of the good weather outside The post DataSaturday Rheinland – Slides first appeared on Under the kover of business intelligence.
This is actually inspired by an article SQL Server Central, which taught me something new. I decided to verify what was in the article and do some research. The summarytl;dr if you change the schema owner, all permissions are dropped.Another post for me that is simple and hopefully serves as an example for people trying to get blogging as #SQLNewBloggers.The ScenarioWe start by creating three logins and their corresponding database users. Think of them as three colleagues with different roles:User1 — will own the schemaUser2 — will be granted access to a tableUser3 — will eventually take over schema ownershipImagine…