Operations | Monitoring | ITSM | DevOps | Cloud

Let AI Run Your Cloud Infra? Ex-VMware & SAP Architects Weigh In. (ft. TechWorld with Nana)

Can you trust AI to run your platform? AI can now spin up production infrastructure in minutes — but speed cuts both ways. In this episode, Nana(TechWorld with Nana) sits down with Doron Grinstein and Dan Wilson, two architects who built, broke, and fixed platforms at VMware and SAP, for a no-hype look at platform engineering in the age of AI.

AI-Powered Quality Control Is Changing Sustainability Reporting in Construction

Sustainability reporting is becoming a critical requirement across the construction industry as regulators, developers, and procurement teams demand more accurate environmental data from manufacturers. Environmental Product Declarations (EPDs), once considered optional documentation, are increasingly being used as a deciding factor in major construction tenders and compliance evaluations.

AI in Insurance Claims Operations: Where Automation Delivers Real ROI

Traditional insurance claims operations are under immense pressure to change. What has shifted now is the margin for delayed results. Today's customers demand faster updates on claims, while insurers need more robust ways to detect sophisticated fraud patterns. The problem is, simply adding more people isn't a sustainable solution when teams are already dealing with complex documentation. Where most insurers rely on legacy systems that involve endless manual handoffs and document-heavy processes, the modern pace requires a change.

Top 5 AI-Powered Database Query Tools for Data Analysts

Data analysts spend a large part of their workday translating business questions into database logic. A stakeholder asks why revenue changed. A product manager wants to compare cohorts. A finance team needs a variance explained. The question may sound simple, but the path to the answer often involves finding the right tables, understanding how fields are defined, writing SQL, validating joins, checking filters, and making sure the result matches the intended business meaning.

Your developers are using AI agents, your data exposure just multiplied

Your developers are already using AI agents. GitHub Copilot, Cursor, Claude Code. Not just for autocomplete, but to generate features, run test suites, and iterate across branches. Each agent needs a database to work against. And in most organizations, nobody has checked what's actually in that database, or whether it should be there.

Preview launch: the Agent Impact Leaderboard and the Business Impact & ROI Dashboard

The Agent Impact Leaderboard and the Business Impact & ROI Dashboard are live in preview inside GitKraken Insights today. We built them because the questions engineering leaders are getting asked about AI shifted faster than the tools to answer them. Here’s what shipped and how to get access.

Your agent can't fix what it can't see

Agents are getting better and better at fixing bugs. They’re even getting better at testing their work, thanks to headless browsers, sandboxes, simulators, etc. But what about the bugs that only show up once you bring in different browsers, languages, extensions, internet speeds, and all the other variables that get mixed in the second you ship to prod? Or all the bugs that only show up when you account for… well, humans being humans and doing weird stuff you didn’t expect them to do?

Measure the real impact of AI coding tools on software delivery with Datadog AI Impact

Engineering teams have rapidly adopted AI coding tools, but organizations still struggle to understand their impact. Existing dashboards focus on activity, such as daily active users, acceptance rates, or lines of generated code, but these metrics don’t answer a more important question: Are teams actually shipping more, faster, and with fewer issues?