Operations | Monitoring | ITSM | DevOps | Cloud

The software world has spent the last two years obsessed with one question: “Will AI replace developers?” Wrong question. The right question is: “How do developers stay in control while AI becomes part of the toolchain?” Welcome to The Builders Era, where the craft of software development and AI’s computational power meet on developer terms. Not as a replacement narrative. Not as a threat to our profession.

For SREs juggling multiple services, third-party dependencies, and constant alerts, a critical service slowdown can quickly turn into chaos. APM Dashboards may show everything is fine, yet users are still experiencing problems. That gap—between application telemetry and real-world performance—can turn a five-minute fix into a two-hour war room. ‍

Datadog App Builder is a low-code tool for creating internal apps, making use of a drag-and-drop interface that allows engineering teams to troubleshoot issues, optimize operations, and enable self-service while connecting directly to their Datadog data and permissions. Now, with conversational AI, teams can go from idea to working prototype even faster.

AI agents can interact with the world using tools. Those tools can be generic or specific. For example: Generic: Specific: The most general ones, like “run a bash command” and “read and write files” are built into the agent. More specific ones are provided through Model Control Protocol (MCP) servers. Every tool provided to the agent comes with instructions sent as part of the context.

I recorded a video showing how to properly set up TrackJS for a new production website, specifically CertKit, our new certificate lifecycle management tool. The key to effective error monitoring isn’t just installing the tracking snippet, it’s configuring the system to surface real issues while filtering out the noise. I configure a forwarding domain (errors.certkit.io) to bypass ad blockers that might prevent error reporting.

Outages are less common than they once were, but when they happen, the impact is severe. According to the Uptime Institute Global Data Center Survey 2025, half of data center operators reported at least one impactful outage in the past three years, and one in ten of those caused a serious or severe disruption. The financial risk is just as significant. 20% of operators said their most recent outage cost more than $1 million when accounting for downtime, recovery, and reputational damage.

In e-commerce, we all know that speed = money. I know it, you know it, Amazon knows it, eBay knows it, Shopify knows it, everyone knows it. In this article we’ll see how we can improve the perceived performance of our site’s critical pages, like the Product Details page, the Cart page, the Checkout page. We’re going to use the Speculation Rules API (SRA) to prerender/prefetch them, and also explain how certain frameworks like Next.js offer their own prefetching mechanisms.

When I was working at Shopify, Black Friday and Cyber Monday were our Superbowl. We initiated code-freeze weeks before to make sure merchants wouldn't have any unexpected issues during one of the most important times of the year. Sometimes, though, you need to ship updates last minute. Picture this: It's Black Friday Eve, 11:47 PM. You've just deployed a new /sale page with 50+ products at discounted prices. Marketing is about to email 500,000 subscribers. Everything tested fine with your sample data.

When you’re debugging a tricky issue in a distributed system, “what changed?” is often the most important question. You add logs, you capture data, you redeploy, and suddenly your browser is full of open tabs, copied JSON blobs, and screenshots of log lines. Comparing behavior between two requests, two users, or two releases turns into a manual, error-prone chore. Lightrun Snapshots were built to fix the data collection side of that story.

At Coralogix, we’re constantly looking to evolve the measurements we take to better understand the efficiency of our infrastructure. We constantly assess and investigate sources of cost in our cloud infrastructure, to ensure we’re getting the best return on investment. This activity, often referred to as FinOps, is becoming a cornerstone of engineering teams.