AI-assisted development is changing how software gets built. Developers can now generate code, architectures, and even entire services from a prompt in seconds. But once that code reaches production, understanding what’s actually happening at runtime is still one of the hardest parts of modern development.

Today’s developers are responsible for far more than writing code—they’re deploying services, integrating AI models, rolling out features, and debugging complex distributed systems. When something breaks, they’re often forced to leave their development environment and jump between dashboards, logs, and monitoring tools just to understand what went wrong.

What if runtime insight was available directly inside the developer workflow?

In this session, we’ll explore how Dynatrace brings real-time observability into AI-assisted development environments using the Model Context Protocol (MCP). Developers can interact with runtime telemetry—logs, traces, metrics, and feature rollouts—using natural language directly from their IDE. AI-powered analysis automatically correlates telemetry and surfaces root cause insights with suggested remediation.

You’ll see how embedding observability into AI-driven development workflows helps developers stay in the flow, debug faster, and ship reliable cloud-native and AI-native applications with confidence—without switching tools or leaving their coding environment.