The development of agentic architectures for artificial intelligence (AI) has ledto new models of interaction and automation, most notably AI agent-based workflows andagent-to-agent (A2A) communication. This paper presents a comparative study of botharchitectures, analyzing their design principles, human-AI coordination capabilities, scalabilityand adaptability in different environments, as well as obstacles and challenges. The analysisshows that while agentic workflows provide efficiency and control in stable environmentswith well-defined processes, A2A architectures excel in distributed and heterogeneouscontexts, offering greater flexibility and autonomy. Based on the premise that technologyshould strengthen, not replace, human capabilities, this study aims to generate a significantimpact in the areas where it is most needed. Representative examples from the education,healthcare, and i...