Operations | Monitoring | ITSM | DevOps | Cloud

Evaluating our AI Guard application to improve quality and control cost

This article is part of our series on how Datadog’s engineering teams use LLM Observability to build, monitor, and improve AI-powered systems. Organizations are building AI agents that help users automate work, analyze data, and interact with complex systems through natural language. As these agents become more capable, they also become more complex and exposed to risks such as prompt injection, data leaks, and unsafe code execution.

From Chef to Chief Architect: Navigating the Intersection of AI and Data Security | Harness Blog

In the world of enterprise software, the transition from traditional DevOps to modern AI-driven delivery is less like a flip of a switch and more like a high-stakes kitchen. As Devan Shah, Chief Architect at IBM, puts it: the ingredients have changed from food to code, but the need for a precise, governed process remains the same.

Getting started with Claude Code and CircleCI

AI-powered coding tools are changing how developers work. Tools like Claude Code can write functions, refactor code, and build features through natural conversation, often faster than you could type them yourself. But speed creates its own risks. AI-generated code can contain subtle bugs, reference packages that don’t exist, or misuse APIs in ways that only surface at runtime. That’s where continuous integration comes in. CI is a safety net that lets you move fast confidently.

AI Assistant vs Skylar Advisor

What happens when AI understands your entire environment? With Skylar Advisor, you move beyond prompts and responses and get prioritized guidance based on real operational impact. Skylar Advisor identifies what matters most, explains why it matters, and provides clear next steps so even junior IT professionals can operate with confidence.

Getting started with Gemini and CircleCI

AI coding assistants like Gemini are changing how developers write code. They can generate entire functions, debug tricky issues, and help you move faster than ever before. But with that speed comes a new challenge: how do you make sure AI-generated code actually works? AI assistants are powerful, but they’re not perfect. They can introduce subtle bugs, miss edge cases, or generate code that breaks existing functionality. That’s where CI (continuous integration) comes in.

The path to self-healing: Re-architecting for massive scale on kubernetes

In the world of network assurance, even a few seconds of delay can result in significant business losses. In this session from Civo Navigate India, Dr. Shivananda R Poojara (Head of Cloud Business Unit, Airowire Networks) explains how his team dismantled a massive monolithic service stack and rebuilt it for a high-performance, cloud-native era in just 75 days.

Why Nexthink Intelligence Is a Game-Changer for IT Teams

Nexthink Intelligence transforms digital employee experience (DEX) for modern enterprises. Learn how IT teams can leverage real-time analytics, proactive insights, and automation to improve user productivity, troubleshoot issues fast, and deliver better workplace tech experiences. Learn more at nexthink.com.

Trends Shaping Cross-Border Tech Recruitment in 2026

Here's the reality: distributed engineering teams have moved from bold experiment to business-as-usual. The challenge? Hiring globally in 2026 has gotten messier than ever before. Compliance rules keep morphing beneath your feet. AI recruiting tools that promised to simplify your life have introduced surprising complications.
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Cisco Live'26 - Amsterdam: Aligning with the AI-Driven Future

The energy at Cisco Live EMEA in Amsterdam (February 9-13, 2026) was primarily driven by groundbreaking AI announcements, & the event provided Fabrix.ai an opportunity to strengthen our strategic position alongside Cisco and Splunk ecosystems. The event’s focus on AI, highlighted by the recent Cisco AI Summit, emphasizes a clear market direction in which Fabrix.ai is perfectly poised to accelerate innovation.