Problem
Research questions and friction points this paper is trying to address.
AI agents
agent-based architectures
practitioner adoption
LLM-driven systems
industrial practice
Making Sense of AI Agents Hype: Adoption, Architectures, and Takeaways from Practitioners
🤖 AI Summary
This study addresses the current lack of systematic understanding in academia regarding the practical design and deployment of AI agent systems in industry. Through qualitative analysis of 138 practitioner conference talks, this work presents the first comprehensive synthesis of prevailing architectural paradigms, representative application scenarios, and enabling technology stacks for industrial-scale agents powered by large language models. The research identifies several reusable architectural patterns and implementation strategies, thereby bridging the knowledge gap between academic inquiry and industrial practice. By grounding its findings in real-world deployments, this paper offers practitioners empirically informed guidance for designing effective AI agent systems.
📝 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).
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI agents
agent-based architectures
LLM-driven systems
practitioner insights
architectural patterns
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