Operations | Monitoring | ITSM | DevOps | Cloud

Build a multi-agent AI system using CrewAI, Gemini, and CircleCI

Multi-agent AI systems are trending in the software development industry right now. These systems consist of a group of individual agents that collaborate to achieve a desired goal. They mimic real world teams and departments in how they are organized. In multi-agent AI systems, each agent is assigned a task that is required to achieve a final output.

It's Never Different This Time: LLM Reliability Without the Hype with Julien Simon

In this episode, Julien Simon, longtime voice in the open-source ML world, reminds us that even in the era of GenAI, reliability fundamentals haven’t changed. Julien breaks down why calling “the same model” from different providers can produce wildly different results, how deployment choices introduce hidden variability, and why reliability teams need to think of LLM systems as distributed systems.

Technology as a Personal Finance Partner

Technology has evolved into much more than a convenience-it's become a true financial companion. From apps that track spending in real time to platforms that automate savings and investments, digital tools are transforming the way people handle money. For many, these innovations have brought clarity and control to what used to feel like an overwhelming process.

Boost Developer Experience with LLMs!

Your laptop is powerful enough to run your own LLM. Here's why that matters While centralized AI tools help teams, they miss something critical: your personal knowledge. Meeting notes, tips, tricks, and context only you have. Kyle Fransham shows how running a local LLM changes the game. Index your own "master document of knowledge" and query it right in your dev environment. No cloud needed. The tools are accessible. The setup is simple. And the impact? Game-changing for how you work.

Agentic AI: Ushering in the Next Era of Intelligent IT

IDC predicts agentic AI will command over 26% of global IT spend, hitting $1.3 trillion in 2029. How do IT Ops teams prepare for the reality of agentic systems being embedded across workflows, interfaces, and enterprise platforms? We went straight to the source—IT Ops leaders—to learn how they’re tackling agentic AI.

Ep 18: AI has a memory problem, just like you do

In this episode of Masters of Data, we dive into how AI learns, examining both how we teach it and what it derives from human performance, as well as why context plays a crucial role in AI interactions. We break down five key components of AI training and talk about why we should view AI as a tool under human control rather than an autonomous entity. We explore the challenge of maintaining context in AI—much like our own memory struggles—and discuss methods, such as retrieval-augmented generation, that can help AI retain context more effectively.

Introducing Kentik AI Advisor

Introducing Kentik AI Advisor. AI with a comprehensive understanding of your network that thinks critically and advises how to design, operate, and protect infrastructure at scale. With the rise of hybrid cloud networks and the growing demands of AI infrastructure, network teams are under pressure to balance cost, performance, and security, often with limited resources that delay critical strategic initiatives.

Maintaining Software Excellence in the Age of AI Coding Assistance

In this preview of his AWS re:Invent session, Cortex CTO & Co-Founder Ganesh Datta breaks down how AI coding assistants are transforming software development, and what high-performing teams are doing to keep speed and reliability in balance. You’ll learn: If you care about AI, engineering velocity, or building sustainable systems, this is a must-watch. Full Session: December 3 at 2:30 PM Learn more about Cortex: go.cortex.io/reinvent.

When Bots Grow Brains: RPA and Agentic AI For the Win

For a long time, robotic process automation (RPA) was the fastest way to scale repetitive digital work. Bots copied, clicked, and executed rule-based tasks faster than any human. They reduced error rates and delivered early wins for efficiency. Sounds just fine, right? Prepare for a Matrix moment, because the truth is that IT teams built RPA only for predictability. It could follow instructions, but it couldn’t adapt when something unexpected happened.