Operations | Monitoring | ITSM | DevOps | Cloud

Five key takeaways from EDUCAUSE 2025: Adopting AI while navigating change

Having just returned from the 2025 EDUCAUSE Annual Conference in Nashville, I want to share some insights on the future of campus IT from the higher education technology leaders in attendance. Every year, this conference provides an opportunity for technology providers and higher ed professionals to connect and explore the latest innovations in higher education technology. Two themes emerged as critical priorities.

Search Telemetry Without Limits in a Multi Cloud and AI World

Cribl Search gives you one lens across all your telemetry data no matter where it lives. Instead of forcing teams to move data into one system or jump between tools, you get a familiar pipe based query experience with dashboarding and alerting built in. Storage and query processing stay separate so you decide where your data lives while your users get fast, simple access in one place.

Episode 1 - Preparing the workforce for AI | The Intelligent Enterprise

In our first podcast episode of The Intelligent Enterprise, Ricardo Costa, Senior Vice President and Chief Technology Officer at Purolator, gives us his views on how to prepare the workforce for AI. In his role as a technology "translator" connecting business strategies with tech implementations, Ricardo highlighted the importance of translating complex tech concepts into simple, understandable stories and addressing leadership challenges in preparing the workforce for AI, including upskilling and ethical considerations.

AI Observability: How to Keep LLMs, RAG, and Agents Reliable in Production

AI observability closes the gap between “something’s wrong” and “here’s what to fix.” If you run AI in production, you might have felt the whiplash. Yesterday, your LLM answered in 300 milliseconds (ms). Today p99 crawls, costs spike, and nobody’s sure if the culprit is model behavior, data freshness, or GPUs stuck at the ceiling. Dashboards light up, but they don’t tell you which issue puts customers at risk. That’s the gap AI observability closes.

What Are AI Workloads? Everything Ops Teams Need to Know

AI workloads break every assumption you have about infrastructure management. AI is everywhere. Machine learning-based tools are answering customer service questions, accelerating incident resolution, catching fraudulent transactions, spotting defects on production lines, and powering late-night searches that delve into the random topic that pops into your head right before bedtime. Behind every prediction, response, or generated sentence is massive computing power doing serious, continuous work.

AI Monitoring, Explained: Challenges, Core Components, and Why Observability Is the Next Step

Monitoring AI systems isn’t business as usual. Monitoring AI isn’t like monitoring traditional systems. You can’t just track uptime or response times and call it a day. AI models evolve, data shifts, and behavior drifts over time, which means your monitoring has to evolve, too. If you’re running AI workloads in production, you already know this. Your models might look healthy according to your infrastructure metrics, but they’re still making bad predictions.

The Human Touch in AI Chatbots: Balancing Automation and Personalization

Artificial intelligence (AI) is transforming how companies engage with customers. Businesses are increasingly expected to provide instant, accurate, and personalized responses across multiple channels, from websites and apps to social media platforms. AI chatbots have emerged as essential tools in meeting these expectations, enabling businesses to streamline communication, reduce response times, and provide consistent support around the clock.

Audio to Text: Enhancing Collaboration and Documentation for Distributed Tech Teams

In the age of cloud computing, DevOps, and distributed IT operations, remote technology teams are now the norm. Global teams bring exceptional talent but also face unique challenges-language barriers, time zone hurdles, incomplete documentation, and gaps in institutional knowledge. As organizations increasingly rely on virtual meetings and asynchronous communication, the demand for reliable audio-to-text solutions is surging.

Better integration tests in Cursor using proxymock

Cursor is fantastic at cranking out code changes. I recently used it to splice a brand-new downstream API call into one of our Go microservices, and the diff looked great. The unit tests finished before I lifted my coffee mug, yet I still had zero certainty the change would survive contact with real traffic. That gap is all about integration tests, so I paired Cursor with proxymock and the outerspace-go demo service to prove the behavior end to end.