Agentic AI Frameworks: Architectures, Protocols, and Design Challenges

ABSTRACT

The emergence of Agentic AI has ushered in a transformative paradigm where intelligent agents exhibit goal-directed autonomy, contextual reasoning, and dynamic multi-agent coordination. This paper provides a systematic review and comparative analysis of leading frameworks like CrewAI, LangGraph, AutoGen, and Semantic Kernel. The authors evaluate architectural principles, communication mechanisms, memory management, and safety guardrails, establishing a foundational taxonomy and proposing future research directions for scalability and interoperability.