Operations | Monitoring | ITSM | DevOps | Cloud

The question "what do you use for AI agent infrastructure?" has become one of the most searched queries in the DevOps and platform engineering space. And for good reason: the global AI agent market is projected to grow from $5.1 billion in 2024 to $47.1 billion by 2030, representing a compound annual growth rate of nearly 45%. With 85% of enterprises expected to implement AI agents by the end of 2025, getting the infrastructure right has never been more critical.

Every time a new feature goes live, teams hold their breath. Web apps are updated faster than ever, and expectations keep rising. In fact, software bugs cost teams up to 25 times more to fix after release than during development, reinforcing how expensive missed issues can be for small teams trying to grow. Many teams push updates weekly or daily. At first, manual testing feels manageable.

I recently tried using AI to create a digital twin of myself. I uploaded a photo, expecting a futuristic, slightly improved version of me... and what did I get in return? A picture of Kim Jong Un. Clearly, AI has a sense of humor-or a very different definition of "twin." Forget Arnold Schwarzenegger and Danny DeVito. Digital Twins 2-Now Starring My AI Doppelgänger From Speedscale's perspective, a digital twin is built from real production traffic, continuously updated, and executable in your test and CI/CD environments.

Operating a medical practice is already hectic enough without the fear of whether your clinic software is going to be running the next day when you get in the door. However, the truth is as follows: 24-hour downtimes not only are irritating, but also may disrupt the care of patients, schedule appointments, and make the work of those who have to use that system operational at 7 AM a nightmare.