Operations | Monitoring | ITSM | DevOps | Cloud

Transitioning LLM agents from intriguing prototypes to reliable, production-grade solutions introduces a unique and significant challenge: the inherent stochasticity of LLMs. Unlike conventional software, where inputs predictably yield precise outputs, an LLM’s response can exhibit variability even when presented with identical prompts. To ensure the dependability of your LLM agent, you will need a rigorous validation strategy.

Integrating artificial intelligence (AI) into web applications can significantly enhance user experience. AI offers features like image recognition to process and analyze user-uploaded images. Combining this with a robust continuous integration and continuous deployment (CI/CD) pipeline using CircleCI ensures seamless updates and reliable delivery. In this article, you will learn how to build a React app that uses TensorFlow.js for client-side image recognition and set up automated testing with CircleCI.

I recently wrote about the impending SaaS crisis, driven by companies’ newfound ability to use AI to build software they used to have to buy. I predicted this phenomenon would make it even harder for SaaS vendors to drive growth, and that elite SaaS margins would fall from the mid-70s to the mid-60s as companies leaned more into their data and AI.

Downtime is brutally expensive and damaging. Enterprises can lose about $9,000 every minute systems are down, while smaller businesses lose hundreds of dollars per minute. A single outage can often cost over $100,000, and nearly a third of companies lose customers due to downtime. That’s why many organizations turn to platforms like SolarWinds to maintain reliable systems and minimize the risk of costly disruptions.

If you’re building high-performance systems, Golang has probably earned a spot in your stack. Its speed, lightweight concurrency, and quick compile times make it ideal for scalable APIs, microservices, and distributed systems. But those same qualities that make Go powerful can make performance monitoring tricky. Goroutines run fast and in parallel, which means a simple CPU or memory graph doesn’t always tell you what’s slowing things down.

Stay in the loop on key incidents with Auto-Watch and ensure data consistency at every milestone with Required Fields by Incident Type.

Here's a scenario every on-call engineer knows too well: a critical incident fires for a service you’ve never seen before. Your first ten minutes are a frantic scramble across wikis and Slack channels just to answer the most basic questions: Who owns this? What does it do? Where are the runbooks? By the time you’re oriented, the incident has escalated.

Building a catalog used to be a project. It meant months of tracking down owners, untangling dependencies, and manually piecing together a picture of your architecture. It was a tedious, thankless process that delayed the value of your Internal Developer Portal (IDP) before you even got started. Now, it’s a coffee break. We’re excited to introduce Magellan, our new AI-powered data engine designed to build your catalog and get your IDP live in minutes.