Making Sense of AI Agents Hype: Adoption, Architectures, and Takeaways from Practitioners
Ruoyu Su, Matteo Esposito, Roberta Capuano, Rafiullah Omar, June Sallou, Henry Muccini, Davide Taibi
TL;DR
This paper reviews 138 practitioner talks to understand real-world AI agent architectures, adoption patterns, and application domains, providing insights into industrial practices and strategies.
Contribution
It offers a comprehensive analysis of practitioner perspectives on AI agents, highlighting common architectures, adoption trends, and application areas in industry.
Findings
Identified prevalent architectural strategies in AI agent deployment
Mapped application domains and technologies used in industry
Revealed common patterns and challenges in AI agent adoption
Abstract
To support practitioners in understanding how agentic systems are designed in real-world industrial practice, we present a review of practitioner conference talks on AI agents. We analyzed 138 recorded talks to examine how companies adopt agent-based architectures (Objective 1), identify recurring architectural strategies and patterns (Objective 2), and analyze application domains and technologies used to implement and operate LLM-driven agentic systems (Objective 3).
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