Agentic AI Frameworks: Architectures, Protocols, and Design Challenges

2025-08-13

AI Summary

This work addresses the lack of systematic comparison and classification frameworks for mainstream Agentic AI systems. We conduct a structured literature review and cross-framework analysis of CrewAI, LangGraph, AutoGen, and related platforms, focusing on architecture design, inter-agent communication mechanisms (e.g., CNP, A2A, ANP), memory management, security provisions, and alignment with service-oriented computing paradigms. Our method yields the first layered taxonomy for Agentic AI systems, explicitly identifying scalability, robustness, and interoperability as core bottlenecks. Innovatively, we elevate communication protocols to a primary taxonomic dimension—revealing three persistent open challenges: protocol heterogeneity, inconsistent context propagation, and weak service-contract enforcement. Based on this analysis, we propose a novel research direction: service-oriented agent collaboration protocols. This contribution provides both theoretical foundations and practical guidelines for standardizing and engineering autonomous AI systems. (149 words)

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The emergence of Large Language Models (LLMs) has ushered in a transformative paradigm in artificial intelligence, Agentic AI, 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 Agentic AI frameworks, including CrewAI, LangGraph, AutoGen, Semantic Kernel, Agno, Google ADK, and MetaGPT, evaluating their architectural principles, communication mechanisms, memory management, safety guardrails, and alignment with service-oriented computing paradigms. Furthermore, we identify key limitations, emerging trends, and open challenges in the field. To address the issue of agent communication, we conduct an in-depth analysis of protocols such as the Contract Net Protocol (CNP), Agent-to-Agent (A2A), Agent Network Protocol (ANP), and Agora. Our findings not only establish a foundational taxonomy for Agentic AI systems but also propose future research directions to enhance scalability, robustness, and interoperability. This work serves as a comprehensive reference for researchers and practitioners working to advance the next generation of autonomous AI systems.

Problem

• Analyzing architectures and protocols of Agentic AI frameworks
• Evaluating communication mechanisms and safety in multi-agent systems
• Identifying challenges for scalability and interoperability in AI agents

Innovation

• Systematic review of Agentic AI frameworks
• Analysis of agent communication protocols
• Proposal for future research directions