New framework for AI agent design patterns presented
Researchers have introduced a new two-dimensional framework for classifying AI agent design patterns, combining cognitive functions with execution topologies.
What happened?
A new framework for AI agent design patterns has been published on arXiv. The framework, 'A Two-Dimensional Framework for AI Agent Design Patterns: Cognitive Function and Execution Topology', aims to systematise the design of AI agents based on large language models (LLMs). It combines two axes: Cognitive Function with seven categories and Execution Topology with six structural archetypes.
Key facts
| Publikationsdatum | 24 maj 2026 |
|---|---|
| Antal kognitiva kategorier | 7 |
| Antal exekveringsarketyper | 6 |
| Antal namngivna mönster | 27 |
| Nya mönster | 13 |
”Existing frameworks for LLM-based agent architectures describe systems from a single perspective: industry guides (Anthropic, Google, LangChain) focus on execution topology -- how data flows -- while cognitive science surveys focus on cognitive function -- what the agent does.”
”We propose a two-dimensional classification that combines (1) a Cognitive Function axis with seven categories (Context Engineering, Memory, Reasoning, Action, Reflection, Collaboration, Governance) and (2) an Execution Topology axis with six structural archetypes (Chain, Route, P”
”The resulting 7x6 matrix identifies 27 named patterns, 13 with original name”
Why it matters
Current classifications have focussed either on data flow (execution topology) or agent tasks (cognitive function). According to the authors, the new framework addresses this deficiency by providing a more comprehensive understanding of AI agent architecture, enabling improved analysis of failure modes and design choices.
Who is affected?
The framework is relevant for AI researchers, developers working with LLM-based agents, and systems architects. It facilitates communication across disciplines and contributes to more standardised development of AI systems.
What else you should know
The framework identifies 27 named design patterns for AI agents, 13 of which are entirely new. This provides a more detailed taxonomy than previous, one-dimensional classifications.
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