Agentic AI in Practice: A Comprehensive Review of Framework Architectures, Coordination Models, and Workflow Design

The rapid evolution of large language models has accelerated the emergence of Agentic AIsystems capable of autonomous reasoning, planning, and tool execution. Unlike traditionalreactive AI models, agentic systems operate through structured orchestration frameworks thatcoordinate multi-step workflows, memory management, and inter-agent collaboration.However, the growing ecosystem of agentic frameworks presents fragmented architecturalphilosophies, execution strategies, and coordination paradigms. This paper provides acomparative analysis of prominent Agentic AI frameworks, focusing on their executionmodels, orchestration mechanisms, and implementation patterns. Specifically, we examinerole-based sequential systems such as CrewAI, graph-based stateful workflows implementedin LangGraph, conversational multi-agent loops exemplified by AutoGen, planner-drivenautonomous architectures such as Agno, and visual node-based automation platforms like n8n.We categorize these frameworks according to their execution semantics, coordinationstructure, extensibility, and suitability for real-world deployment scenarios. Through structuredcomparison, we identify core architectural trade-offs between flexibility, controllability,scalability, and transparency. The analysis further highlights challenges related to evaluationstandardization, reliability, and integration into enterprise workflows. By synthesizingarchitectural patterns across frameworks, this study proposes a unifying perspective on agenticorchestration strategies and offers guidance for researchers and practitioners designing robustautonomous AI systems. The findings contribute to a clearer taxonomy of Agentic AI executionmodels and inform future research directions in scalable, safe, and interpretable autonomousintelligence.