Meta-Agent Generation: A User-Adaptive Framework for Generating Personalized Artificial Intelligence Agents
Abstract
Artificial intelligence (AI) agents are increasingly designed to perform tasks through planning, tool use, memory, environmental interaction, and autonomous execution. However, conventional approaches generally require users to select, configure, or adapt agents from predefined systems. Although personalization can modify instructions, interaction styles, or selected parameters, the underlying agent architecture often remains largely unchanged. This paper proposes Meta-Agent Generation (MAG), a conceptual framework in which an upper-level generative system constructs, composes, configures, or adapts an AI agent according to the requirements of an individual user. Rather than merely personalizing the outputs of an existing agent, MAG treats the agent itself as the object of generation.
The proposed framework represents user requirements as a structured specification covering objectives, capabilities, interaction preferences, available resources, privacy constraints, permitted autonomy, and expected reliability. A meta-agent generator translates this specification into a candidate agent architecture, selects appropriate models and tools, determines memory and planning mechanisms, defines operational permissions, evaluates feasibility, and produces an agent tailored to the individual user. The framework supports multiple generation levels, ranging from instruction and configuration generation to component composition, model adaptation, and more extensive agent construction. It also incorporates feedback-driven revision, enabling an agent to evolve as the user's requirements change.
This paper formalizes the problem, introduces a modular architecture, defines an optimization objective, and outlines an experimental methodology for comparing personalized agent generation with conventional agent selection, prompt-based personalization, and fixed-architecture adaptation. The framework is conceptual and has not yet been empirically validated. Its principal research question is whether generating the structure and operational mechanisms of an agent from individual requirements can produce more useful, efficient, and controllable agents than personalizing only the behavior of predefined agents.
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BibTeX
@misc{zhang2026metaagent,
title = {{Meta-Agent Generation: A User-Adaptive Framework for Generating Personalized Artificial Intelligence Agents}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23499849},
url = {https://zenodo.org/records/23499849},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Meta-Agent Generation: A User-Adaptive Framework for Generating Personalized Artificial Intelligence Agents
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/20
PB - Zenodo
DO - 10.5281/zenodo.23499849
UR - https://zenodo.org/records/23499849
AB - Artificial intelligence (AI) agents are increasingly designed to perform tasks through planning, tool use, memory, environmental interaction, and autonomous execution. However, conventional approaches generally require users to select, configure, or adapt agents from predefined systems. Although personalization can modify instructions, interaction styles, or selected parameters, the underlying agent architecture often remains largely unchanged. This paper proposes Meta-Agent Generation (MAG), a conceptual framework in which an upper-level generative system constructs, composes, configures, or adapts an AI agent according to the requirements of an individual user. Rather than merely personalizing the outputs of an existing agent, MAG treats the agent itself as the object of generation. The proposed framework represents user requirements as a structured specification covering objectives, capabilities, interaction preferences, available resources, privacy constraints, permitted autonomy, and expected reliability. A meta-agent generator translates this specification into a candidate agent architecture, selects appropriate models and tools, determines memory and planning mechanisms, defines operational permissions, evaluates feasibility, and produces an agent tailored to the individual user. The framework supports multiple generation levels, ranging from instruction and configuration generation to component composition, model adaptation, and more extensive agent construction. It also incorporates feedback-driven revision, enabling an agent to evolve as the user's requirements change. This paper formalizes the problem, introduces a modular architecture, defines an optimization objective, and outlines an experimental methodology for comparing personalized agent generation with conventional agent selection, prompt-based personalization, and fixed-architecture adaptation. The framework is conceptual and has not yet been empirically validated. Its principal research question is whether generating the structure and operational mechanisms of an agent from individual requirements can produce more useful, efficient, and controllable agents than personalizing only the behavior of predefined agents.
ER -