What agentic AI borrowed from microservices (and made worse)

The text explores the parallels between past technological transitions, such as the move to cloud-native environments and microservices architectures, and current developments in the AI space, particularly with AI agents. It highlights how many core challenges, like managing complexity, orchestrating systems, and handling state and time, have persisted across these transitions. The text discusses the evolution from monolithic systems to microservices and now to microagents in AI, emphasizing the benefits of breaking down large systems into smaller, more manageable components. It also touches on the shift from request/response models to event-driven architectures, as well as the importance of effectively managing short-term memory and state in distributed systems. The narrative underscores the value of applying lessons learned from previous experiences in microservices to the current AI landscape, advocating for a thoughtful approach to leveraging past insights while adapting to new capacities offered by AI technologies.