Agentic AI systems are rapidly emerging as a new paradigm – shifting away from static Large Language Models (LLMs) toward autonomous, goal-oriented entities capable of reasoning, planning, and collaboration. In the past, to integrate AI systems with external applications, developers had to implement unique, task-specific code for each interface. This approach led to limited scalability and imposed a significant burden on the development process. In this view, this paper presents a comprehensive review of two emerging open standards designed to address these limitations: the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol. MCP provides structured and standardized access to tools, data sources, and contextual memory, while A2A communication frameworks define standard mechanisms enabling agents to exchange information, coordinate tasks, deliberate on decisions, and establish collaborative or competitive relationships. This paper analyzes MCP and A2A in terms of their respective roles, architectural features, and their relevance to the development of robust agentic AI systems. We compare their functionalities, identify their complementary strengths, and discuss how each contributes to the broader goal of building scalable, decentralized, and reliable autonomous AI agent environments.