Operations | Monitoring | ITSM | DevOps | Cloud

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.

Observability data arrives as a flood of signals, full of potential, but rarely consistent. Error messages and debug logs can reveal what businesses care about: reliability, customer experience, and revenue. The challenge is turning raw technical events into information the whole organization can act on. Many observability systems store data first and structure it later, forcing teams to rebuild context in dashboards and queries, often duplicating logic across services.

Most companies rewired how their people work, not once but twice. First for remote, then for RTO (Return to Office). Their network monitoring never caught up. So, what happened? IT teams are managing a network that spans headquarters, branch offices, home setups, and cloud apps with tools that still assume everyone's connecting back to one place. When something breaks (and it will), nobody can pinpoint where. IT takes the blame. Users lose productivity. Leadership loses patience.

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.

At Aiven, we use and contribute to open source to build our managed data platform. In the early days of Aiven for PostgreSQL, robust cloud-native tools for high availability and streaming backups did not exist at the level we needed. So we built our own. We created pglookout to monitor replication and handle failovers, and we built pghoard for backup and restore using object storage. These tools served us and thousands of our customers well for years. But the open-source PostgreSQL ecosystem has grown.

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.

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.

Being productive is another thing that people may find challenging yet it is not always that they are not trying. Their failure is due to the invisibility of their work. Tasks live in scattered notes and emails that are received in your inbox are piling up. Vital messages lose their way amidst the noise and you can change priorities almost on a daily basis.

Google's Core Web Vitals (CWVs) measurements have been used by web administrators and SREs to review frontend application performance metrics, and have been factored into Google's page rankings since 2021. They are also used in Google Analytics, which crawls websites and evaluates performance metrics over a period of multiple days, and with various frontends (desktop web, mobile web, etc.) to establish how well a website performs in production.

According to CloudZero’s Cloud Economics Pulse, databases are often among the largest and most persistent cloud cost categories. Database costs are notoriously difficult to predict and control. Unlike stateless infrastructure that scales predictably with traffic, databases run continuously and expand behind the scenes, causing costs to rise even when usage appears stable. Because databases run continuously and expand behind the scenes, costs can rise even when usage appears stable.