arXiv: Making Sense of AI Agents Hype — 138 Practitioner Talks Reveal Real Adoption Patterns

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Researchers analyzed 138 recorded industry talks (arXiv:2604.00189) to examine how companies adopt agent-based architectures, identifying recurring design strategies across application domains. The study bridges the gap between academic agent research and production reality, cataloging which frameworks (CAMEL, AutoGen, MetaGPT, LangGraph, Swarm, MAKER), interaction patterns (chain, star, mesh, workflow graphs), and technologies practitioners actually choose versus what papers recommend. This is the first large-scale empirical study of how agent systems are deployed in practice versus how they are described in research literature.