Researchers propose framework for designing AI agents built on large language models

As AI agents powered by large language models become increasingly common, researchers have struggled to standardize how these systems should be designed and operated. A new paper proposes a comprehensive taxonomy and decision model to address this fragmentation and guide development of foundation model-based agents.

What the research covers#

The work introduces a detailed classification scheme that encompasses both functional capabilities and non-functional qualities of agent architectures. Beyond architectural considerations, the framework also examines the operations and decisions that occur at two distinct phases: during the initial design stage and during runtime execution once the system is deployed.

Why this matters#

The rapid adoption of agent systems across many domains has outpaced the development of standardized design principles. By providing a structured approach to architecture design options and a decision model for critical trade-offs, this taxonomy aims to reduce inconsistency in how these systems are built and operated. The framework helps teams make informed choices about which architectural patterns best fit their specific use case and requirements.

Next steps#

The structured guidance is intended to improve the overall quality and consistency of foundation model-based agent development, offering teams a clearer path from conception through deployment.