The research highlights the complexity of agentic architectures, noting that while individual components have been examined, the interactions within multi-agent systems have not been thoroughly investigated. The study identifies four critical dimensions of agentic systems which impact performance, including orchestration strategies and prompt implementations. This evaluation revealed that even the highest scoring models performed below expectations, achieving only 35.3% success on complex tasks. These findings challenge the assumption that one model fits all, indicating a need for tailored architectural approaches.
Furthermore, the results underscore potential risks associated with deploying current agentic systems in enterprise environments. As the study indicates, despite significant advancements in AI capabilities, the success rates on even simpler tasks show there is room for substantial improvement. Consequently, the research may guide future architectural decisions in designing agentic systems, encouraging a shift towards more empirically backed frameworks. By acknowledging these limitations, organizations can make more informed choices regarding model selection and implementation strategies.
👉 Pročitaj original: arXiv AI Papers
