Researchers Propose New Framework for AI Agent Design Patterns

Researchers have introduced a novel framework for categorizing AI agent architectures by combining two analytical dimensions: cognitive function and execution topology. The system, described in a preprint paper published on arXiv, identifies 27 distinct design patterns by cross-referencing these axes.

Current classification methods “describe systems from a single perspective,” according to the study. Industry guidelines from companies like Anthropic and Google focus primarily on execution topology—the flow of data through systems—while cognitive science approaches emphasize functional capabilities. The new framework addresses limitations in these single-axis approaches by demonstrating how identical execution topologies can implement different cognitive functions, and vice versa.

“The same Orchestrator-Workers topology can implement Plan-and-Execute, Hierarchical Delegation, or Adversarial Collaboration patterns depending on cognitive function,” the researchers noted. This dual-axis approach provides clearer differentiation between architecturally distinct systems.

The framework could influence both academic research and industry practices by offering standardized terminology for agent design. With AI systems becoming increasingly complex, the authors argue that this classification system provides “a common language for analyzing and comparing agent architectures across domains.”