Hybrid System Framework for AI Pipeline and AI Agent
Debanti Giri, Chiranjeevi S. P. Rao Kandula, Maregowdagari Srikanth, Sumitra Srinivas Kotipalli, JMSV Ravi Kumar · 2025
A significant task of comparing two core artificial intelligence (AI) architecture techniques: AI agents and AI pipelines. AI pipelines, which are linear, structured frameworks with sequential, static task execution, handle large-scale data processing. On the other hand, AI agents are independent entities with the ability to interact with changing environments, make choices, and modify their behaviour over time. Through a comparative analysis, we delve into both approaches’ functional capabilities, architectural distinctions, and adaptability. Our study also highlights the advantages and disadvantages of each in practical applications, emphasizing the effectiveness of AI pipelines for batch processing and the adaptability of AI agents for in the moment decision-making. Case studies from various fields, including AI-powered autonomous driving and predictive maintenance employing pipelines, are included in the study. Lastly, we discuss the implications for AI development going forward and the possibility of hybrid models that integrate the best features of both architectures. This comparison analysis aims to underscore the importance of choosing the exemplary architecture based on scalability, adaptability, and operational needs for AI jobs.