Agentic AI Frameworks: Architectures, Protocols, and Design Challenges

Description

A systematic review and comparative analysis of leading Agentic AI frameworks, based on the research paper "Agentic AI Frameworks: Architectures, Protocols, and Design Challenges" (arXiv: 2508.10146). What's covered: Evolution of intelligent agents from classical MAS/BDI systems to modern LLM-powered agents (ReAct, RAISE, Reflexion) Deep-dive into agent communication protocols: MCP, A2A (Google), ANP, ACP (IBM), and Agora Comparative analysis of 10+ frameworks: CrewAI, LangGraph, AutoGen, Semantic Kernel, Agno, Google ADK, MetaGPT, SmolAgents, PydanticAI, LlamaIndex, and OpenAI Agents SDK Memory architectures: short-term, long-term, semantic, procedural, and episodic memory across frameworks Guardrail capabilities and safety enforcement per framework Real-world applications in finance, transportation, and business automation Service computing readiness: W3C standards (WSDL, WS-Policy, BPEL) adoption across frameworks Current limitations: rigid architectures, no runtime discovery, code safety risks, and interoperability gaps Future research directions for scalability, standardization, and composability Keywords: Agentic AI, LLM Agents, Multi-Agent Systems, CrewAI, LangGraph, AutoGen, Semantic Kernel, MetaGPT, MCP, Agent2Agent, Agent Communication Protocol, AI Frameworks