Sub-agentic AI architecture: a conceptual review of specialization and hierarchical orchestration
DOI:
https://doi.org/10.26439/interfases2026.n023.8714Keywords:
agents, architecture, artificial inteligence , ILIA, subagentsAbstract
During the second half of 2025, artificial intelligence (AI) research has shown a shift towards multi-agent architectures, in which sub-agents acquire a central structural role. Unlike monolithic approaches, these architectures allow complex tasks to be broken down into specialized functional units, coordinated by an orchestrating agent, which promotes scalability, parallelism, and more efficient context management. This article defines multi-agent architectures, web agents, and sub-agents. The conceptual framework Multi-Agent Data Mining (MADM), the architecture of the complex task tree, and the kitchen metaphor are analyzed. Benefits in terms of efficiency, resilience, and adaptability are identified, both in scientific environments and on commercial platforms. However, challenges remain related to orchestration, security, computational costs, academic training, and ethical governance. In this context, the Latin American Artificial Intelligence Index (ILIA 2025) is examined as a guide for integrating sub-agentic architectures into educational models with an ethical, interdisciplinary, and sustainable focus, aimed at fostering more responsible and inclusive regional innovation.
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