The evolution from monolithic applications to microservices involved breaking down software into independently deployable units. REST APIs, messaging, and service discovery facilitated scalable systems. This microservices model works well for predictable business logic. However, AI agents introduce a new execution paradigm. Agents receive an objective and then reason about available capabilities to create workflows. This redefines service boundaries to focus on decision-making and orchestration. Traditional microservices pre-define service invocation paths. In contrast, AI agents determine workflows during execution based on an intent. For example, an agent fulfilling a complex order must consider multiple factors before invoking any specific service. The workflow dynamism of AI agents represents a significant shift from static microservice designs.