Operations | Monitoring | ITSM | DevOps | Cloud

Let’s be honest. AI features can sound cool on paper, but too many tools overpromise and underdeliver. At Logz.io, we didn’t want to build “yet another AI chatbot.” We wanted to create something our engineers and yours would actually use when incidents hit, logs explode, or someone asking, “What just happened to production?” Here’s how our AI Agent evolved from a basic chat interface to an incident-resolving, log-analyzing, doc-digging, context-aware assistant.

Ever feel like you’re flying blind in your AWS environment? You’re not alone. In the sprawling universe of microservices, containers, and serverless functions, trying to troubleshoot without proper observability is like trying to find a bug in a datacenter… with the lights off… while wearing sunglasses.

Recently, Logz.io co-founder and CTO Asaf Yigal teamed up with DevOps legend John Willis for an engaging webinar exploring the exciting—and occasionally intimidating—world of Shadow AI and the “tech debt tsunami” on the horizon. This lively session dove into how generative AI (GenAI) is reshaping software development, DevOps practices, and infrastructure management, along with some friendly advice on how organizations can navigate these changes without getting swept away.

Syntax highlighting and auto suggestions to streamline query building and troubleshooting.

Do you ever wish you had a personal data guru who could magically sift through all your data, spot patterns before they become problems, summarize everything in a way that actually makes sense and propose recommendations? Well, meet AI Agents—the “digital teammates” who do all that without demanding coffee breaks.

AWS CloudWatch is a widely used observability tool that comes built into AWS. It provides easy access to logs, metrics, and alarms, making it a convenient choice for teams monitoring AWS workloads. But while CloudWatch offers a lot of power, many teams unknowingly misconfigure or misuse it, leading to unexpected costs, limited visibility, and operational challenges. Here are some common pitfalls we see—and how to avoid them.

As an observability leader, at Logz.io, we pride ourselves on continuous innovation. That’s why, last year, we released our AI agents to revolutionize observability by helping businesses, and their engineering and DevOps teams, automate data analysis and root cause analysis. The primary way in which engineering and DevOps teams interact with the agents is by asking performance, troubleshooting, and optimization-related questions.