## Executive Summary: Dapr Agentic Cloud Ascent (DACA)

The Dapr Agentic Cloud Ascent (DACA) guide introduces a strategic design pattern for building and deploying sophisticated, scalable, and resilient agentic AI systems. Addressing the complexities of modern AI development, DACA integrates the OpenAI Agents SDK for core agent logic with the Model Context Protocol (MCP) for standardized tool use and the Agent2Agent (A2A) protocol for seamless inter-agent communication, all underpinned by the distributed capabilities of Dapr. Grounded in AI-first and cloud-first principles, DACA promotes the use of stateless, containerized applications deployed on platforms like Azure Container Apps or Kubernetes, enabling efficient scaling from local development to planetary-scale production, potentially leveraging free-tier cloud services and self-hosted LLMs for cost optimization. The pattern emphasizes modularity, context-awareness, and standardized communication, envisioning an "Agentia World" where diverse AI agents collaborate intelligently. Ultimately, DACA offers a robust, flexible, and cost-effective framework for developers and architects aiming to create complex, cloud-native agentic AI applications that are built for scalability and resilience from the ground up.