Operations | Monitoring | ITSM | DevOps | Cloud

The rise of large language models (LLMs) and agentic applications promises to transform enterprise workflows. Yet, the core challenge remains: How do we ensure these powerful agents generate accurate, relevant, and trustworthy responses based on proprietary enterprise data rather than relying solely on their generic training knowledge? The answer lies in grounding — connecting the LLM to verified, trusted, and up-to-date information.

For decades now, search engine optimization (SEO) has been viewed as a “cheat code” channel – a method for businesses of any size or budget to achieve organic growth and scale against larger competitors. Industry research over the past two years has valued the SEO industry itself at over $150 billion, and projected to grow by an additional 20% by 2030, as there are thousands of case studies evidencing the value of investing in SEO as a growth channel.

Building automated workflows that adapt to real-world complexity can be a challenge. As systems scale and scenarios multiply, teams often end up hardcoding endless logic branches just to handle every potential outcome. That’s why we’re introducing Datadog Agent Builder, a powerful new tool that lets you create custom AI agents that are fully hosted by Datadog.

Over the past three decades, Ruby has assumed a pivotal role in the modern web stack and become a fixture in the tool kits of countless DevOps and platform teams. Today, it is a driving force in contemporary application development, testing, automation, and CI/CD. For this blog post, we used data from our always-on continuous profiling of more than 3,000 real-world services from hundreds of organizations to track trends in Ruby usage and performance.

Most teams picture incident response as a linear sprint from alert to resolution. A notification appears, an analyst pivots across screens, a decision gets made, and the workflow moves on. It works, but it is mechanical, tiring, and fragile. Graylog 7.0 aims for something more impactful. Guided remediation gives analysts clarity during the moments when pressure rises and context usually scatters. It takes raw detection data and turns it into a clear path forward. No theatrics.

Bitbucket Cloud is more than a code hosting platform. We’re an enterprise partner, helping teams code together at scale with security, compliance, and flexibility at every step. As part of the Atlassian Cloud platform serving more than 300,000 organizations around the world, we’re continuing to build the next generation of Bitbucket Cloud as your trusted cloud vendor, whether you’re a global bank, healthcare provider, or a fast-scaling tech company.

Introducing Kentik AI Advisor, a powerful new AI designed to deeply understand your network, reason through complex issues, and deliver clear, actionable guidance for designing, operating, and protecting your networks. By autonomously querying Kentik’s rich telemetry and tools, it explains what’s happening, why it matters, and what to do next — from troubleshooting and capacity planning to cost optimization and risk mitigation.

‍ Like many developers, I’ve spent the last year adjusting to a world where I’m spending more keystrokes arguing, or, “explaining” to an agent the code I’d like to be written. No longer must we toil, writing every line code by hand like our great grandparents probably did.

Production errors can feel overwhelming. Your Rollbar dashboard is filling up with alerts, your team is scrambling to understand what needs immediate attention, and critical revenue-impacting issues might be buried among less urgent problems. Sound familiar? In this post, I'll walk you through a workflow that transforms production error chaos into organized, prioritized action items. We'll cover everything from analyzing Rollbar errors to creating properly linked Linear tickets.