Dynatrace Blog

Everyone’s trying to understand how their jobs and responsibilities are changing in the age of AI. With so much code generated by AI, how can you trust what you’re shipping to production? At Dynatrace, we believe observability is what turns AI output into something you can actually trust in production. That starts with runtime intelligence inside the places developers already work: the IDE, the terminal, CI, and the workflows where AI assistants now sit beside them.

The idea behind platform engineering is simple: Give developers consistent tools, paved paths, and fewer reasons to fight their way through one-off workflows. The field is maturing quickly as organizations adopt internal developer platforms (IDPs) to accelerate software delivery: 89% of organizations now have an IDP, and 60% report broad adoption. Observability and monitoring dashboards are the leading IDP capability, available in 74% of organizations.

Reliability has entered a new phase. Site Reliability Engineering (SRE) adoption is high, and practices like service level objectives (SLOs) and quality gates are common. But even as automation expands and reliability practices become more sophisticated, many teams are spending more time interpreting signals, supervising AI, and stitching together fragmented data than they expected. The data from the Dynatrace State of SRE 2026 research demonstrates this ongoing source of tension.

Scaling up your observability practice often creates access control challenges. Synthetic monitoring is no exception. As more teams rely on synthetic tests to validate releases, automate operational workflows, and monitor the availability of AI or web services, more users and automated systems need the ability to create and execute synthetic monitors.

From failed checkouts to disputed transactions, when something goes wrong in a digital customer journey, you often need more than metrics or events. You need to see what the user saw, what they did, and what exactly happened in the end-user browser or mobile application. The new Session Replay lets you replay the full experience in context with logs, traces, events, and RUM data.

Today, the world is using LLM applications like never before, from the chatbots, assistants, and AI agents you type a question into and get an answer back from. Behind that simple back-and-forth, LLM applications do much of their work out of view, from retrieving context and calling models to generating responses. Spans make those steps visible, not just in AI applications, but across any sort of distributed system.

Enterprise AI spending has never been higher. But when boards ask their teams to show the return, most organizations struggle to give a clear answer; not because AI isn’t delivering, but because they’re not measuring the right things. Many enterprise AI projects can’t prove their return.

Step outside your home. The water flowing from your tap, the tolls collected on the highway, the gas that heats your stove, and the power delivered to your city. None of it runs on a web app or a mobile dashboard. It runs on something many people have never heard of: operational technology. We live in a world saturated with technology, yet most of us interact with only a thin slice of it: websites, mobile applications, cloud services.

Making sense of that volume of data typically requires extensive filtering, manual exploration, or complex queries. Instead of spending time searching for patterns, teams should focus on understanding and resolving issues. Dynatrace log pattern analysis, now in preview, makes it easy to identify recurring patterns in free-text log data.

Site reliability engineers (SREs) and platform engineers pursue different missions with shared inputs, and AI is transforming how both operate. AI applications are becoming mission-critical, and observability must now span traditional reliability signals in addition to AI health indicators.