Close the observability gap with agentic observability

When a critical application crashes, executives at an IT company don't want to argue about who was at fault. However, in large companies with many different systems, teams are often using separate old-fashioned monitoring tools, each only showing a portion of the story. This makes it hard to discover what caused the problem when services are down.

As companies use more artificial intelligence, mix traditional systems with cloud computing, and need to keep everything running smoothly, the difficulty of finding the root cause is growing. The systems that keep important services up and running are becoming more connected, while the need for quick recovery, efficiency, and good management is increasing.

Some AI programs are beginning to help with some of the work, but they still only have access to the same limited information that humans get from old monitoring systems. A new kind of AI, called agentic observability, could help solve this problem by putting together information about how everything is working in real-time from applications, the cloud, hardware, networks, and AI systems.

This allows AI programs to understand the situation better and fix problems faster. Paul Appleby, who is the boss of Virtana, talks about why old monitoring methods don't work well and how new AI approaches can give IT teams a better understanding of their systems. He also talks about how important it is to know what's happening with data and AI systems as companies grow and become more complex.

The conversation shows the importance of being able to see what's happening in the technology world, make good decisions, and keep systems running smoothly as companies depend more on new ways of doing things.