Autonomous driving paper index
Agentic AI: A Perspective on Architecture, Frameworks and Applications
One-line summary
This review examines the evolution and architectural foundations of agentic artificial intelligence (AI), with a focus on collaborative multi-agent systems for complex task execution.
Engineering notes
Key topics: autonomous driving, deployment, prediction. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
This review examines the evolution and architectural foundations of agentic artificial intelligence (AI), with a focus on collaborative multi-agent systems for complex task execution. The paper analyzes the core components, agent architectures, coordination mechanisms, application domains, and deployment challenges that enable autonomous reasoning and decision-making in real-world environments. To complement the survey, a comparative cryptocurrency market analysis case study is conducted using CrewAI, LangChain, and LangGraph focusing on workflow orchestration characteristics such as tool invocation, task transitions, orchestration depth, and memory integration. The findings are further supported by evidence from real-world financial applications reported in the literature, indicating productivity gains of 50–80% in financial data tasks and up to 20% improvement in stock prediction accuracy, highlighting the growing impact of multi-agent AI systems in market intelligence. The study highlights how architectural design choices influence reasoning continuity, coordination behavior, scalability, and system reliability, providing practical guidance for the design and deployment of agentic AI systems in complex, data-intensive domains.
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