Operations | Monitoring | ITSM | DevOps | Cloud

User Feedback to Pull Request in Minutes with Cursor + Sentry

Apr 14, 2026 By Sentry In Sentry
Cursor Automations + Sentry Triggers: go from user feedback to a pull request automatically. See how to set up an end-to-end workflow that turns feedback into code changes, posts the PR to Slack, and keeps your team in the loop. In this video, we walk through a real-world example using Sentry Docs. A user submits feedback through a widget on the docs site, it lands in Sentry as an issue, and when assigned, a Cursor Automation kicks off. The automation reads the feedback, validates it, generates a PR against the repo, and posts the link in the relevant Slack thread. No manual work required.
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datadog

Offline evaluation for AI agents: Best practices

Apr 14, 2026 By Tom Sobolik In Datadog
If you’re building LLM-powered applications and agents, you’ve probably asked yourself: “How do I know if my changes actually made things better?” You can tweak prompts, adjust temperature settings, or try different models, but it’s not always easy to validate whether version B’s response is better than version A’s. Most teams fly blind in preproduction and rely on user feedback to see how well their application works in the real world.
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qovery

Stopping Kubernetes cloud waste: agentic automation for enterprise fleets

Apr 14, 2026 By Mélanie Dallé In Qovery
Agentic Kubernetes resource reclamation is the practice of using an autonomous control plane to continuously identify, suspend, and delete idle infrastructure across a multi-cloud Kubernetes fleet. It replaces manual cleanup and reactive autoscaling with intent-based policies that act on business state, eliminating the configuration drift and cloud waste typical of unmanaged fleets.
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redgate

The quiet problem underneath modern software delivery: database change at scale

Apr 13, 2026 By Victoria Roe In Redgate
Application delivery has accelerated over the last decade. Modern CI/CD pipelines, automated testing, and cloud infrastructure have already raised the baseline. Now AI-assisted coding tools are compressing timelines further still - developers are writing and shipping code faster than ever.
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(AusBiz) JFrog teams up with Nvidia to manage AI agents

Apr 13, 2026 By JFrog In JFrog
AI agents are making real-time decisions inside enterprises right now; pulling code, accessing tools, executing tasks. But most businesses have zero visibility into what those agents are actually using. In this interview on @ausbizTV, Sunny Rao, SVP APAC at JFrog, explains why the governance gap is one of the biggest risks facing enterprises today; and how JFrog and NVIDIA are building the trust layer to fix it.
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rollbar

From Stack Trace to Probable Cause: AI Root Cause Analysis Is Here

Apr 13, 2026 By Rollbar In Rollbar
You know the drill. An error fires, you get the stack trace, and then you spend the next 45 minutes tracing it backward through four services, two config files, and a deploy that happened three hours ago. You eventually find the root cause, but the path to get there was manual, slow, and entirely dependent on how well you already knew the codebase. We built AI-powered root cause analysis (RCA) for that kind of slog.
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Densify

AI Factories Will Be Won on Efficiency: Why the Kubex + Rafay Partnership Matters

Apr 13, 2026 By Kubex In Densify
The early era for AI was defined by experimentation, standing up isolated environments, and finding the first practical use cases. Today, the conversation is different. Enterprises are no longer asking whether AI matters. They are asking how to scale it sustainably, securely, and economically. That shift is giving rise to the AI factory: a repeatable, governed, production-ready environment where data scientists, platform teams, and application teams can build, train, deploy, and operate AI at scale.
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honeycomb

Optimizing the OpenTelemetry Python SDK for LLM Workloads

Apr 13, 2026 By Alex Boten In Honeycomb
Agentic workloads thrive with precision tooling. Just like developers, they need the rich context, high cardinality, and fast feedback loops that allow them to ask exploratory open-ended questions of their code. But instrumentation is costly, and from the dawn of software, developers have tried to do the most possible with the least amount of resources.
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