Update comprehensive_guide_daca.md

Author: ziaukhan

comprehensive_guide_daca.md

@@ -24,7 +24,7 @@ The **Dapr Agentic Cloud Ascent (DACA)** design pattern is a strategic framework
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 ## The Indispensable Role of Cloud-Native Technologies in Agentic AI Development
 
-**Developing sophisticated AI agents, especially those intended for production environments and widespread use, is deeply intertwined with cloud-native principles and technologies.** This strong connection stems from the **inherent needs of agentic systems: massive scalability** to handle fluctuating user loads and data volumes, efficient management of intensive computational resources (including GPUs/TPUs often required for complex models), and robust deployment mechanisms. Cloud-native architectures, leveraging containers (like Docker), orchestration platforms (like Kubernetes), serverless computing, and microservices, provide the ideal foundation for building, deploying, and managing these complex agent applications. They enable the elasticity, resource optimization, automated CI/CD pipelines for rapid iteration, resilience, and observability crucial for real-world performance. Furthermore, cloud platforms offer vital managed services for data storage, processing, and AI/ML model lifecycles that streamline agent development. While basic agent experimentation might occur outside a cloud-native context, building professional, scalable, and maintainable AI agents effectively necessitates a strong proficiency in cloud-native practices, making it a critical, almost essential, complementary skill set for developers in the field.
+**Developing sophisticated AI agents, especially those intended for production environments and widespread use, is deeply intertwined with cloud-native principles and technologies.** This strong connection stems from the **inherent needs of agentic systems: massive scalability** to handle fluctuating user loads and data volumes, efficient management of intensive computational resources (including GPUs/TPUs often required for complex models), and robust deployment mechanisms. Cloud-native architectures, leveraging containers (like Docker), orchestration platforms (like Kubernetes), serverless computing, and microservices, provide the ideal foundation for building, deploying, and managing these complex agent applications. They enable the elasticity, resource optimization, automated CI/CD pipelines for rapid iteration, resilience, and observability crucial for real-world performance. Furthermore, cloud platforms offer vital managed services for data storage, processing, and AI/ML model lifecycles that streamline agent development. While basic agent experimentation might occur outside a cloud-native context, building professional, scalable, and maintainable AI agents effectively necessitates a strong proficiency in cloud-native practices, making it **a critical, almost essential, complementary skill set for developers in the field**.
 
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