Dynatrace started collecting trace data from applications in 2005. Organizations wanted to know why an application was slow and what was happening exactly. That first generation was mainly manual and technical. "It was about collecting data and understanding what was going on," explains Spitzbart. APM has remained the company's foundation for a long time and remains a core component today.
There are two complementary revolutions happening simultaneously: Observability for Agentic AI: Providing the visibility needed to facilitate AI adoption by making black-box AI systems transparent and debuggable. AI for Observability: Using intelligent insights and automated actions across complex workflows to transform traditional reactive monitoring into proactive, predictive operations. Observability for AI: Making Black Boxes Transparent Consider a modern AI-powered application where each agent interaction involves multiple of the following:
The best example of this and the reason that wrapt was created in the first place, is to instrument existing Python code to collect metrics about its performance when run in production. Since one cannot expect a customer for an application performance monitoring (APM) service to modify their code, as well as code of the third party dependencies they may use, transparently reaching in and monkey patching code at runtime is the best one can do.