A Two-Dimensional Framework for AI Agent Design Patterns: Cognitive Function and Execution Topology

2026-03-16

AI Summary

Existing AI agent architectures are typically described along a single dimension—either execution topology or cognitive function—making it difficult to characterize their design trade-offs and failure modes. This work proposes the first two-dimensional classification framework that orthogonally integrates cognitive functions (seven types, e.g., perception, memory, reasoning) with execution topologies (six types, e.g., chain, parallel, routing), yielding a 7×6 matrix that systematically defines 28 design patterns, including 15 newly named ones. Through cross-domain validation in finance, legal reasoning, network operations, and medical triage, the study distills five empirical guidelines for pattern selection and establishes a principled, framework- and model-agnostic terminology. This significantly enhances the describability and reusability of agent architectures.

Abstract

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. Neither axis alone disambiguates architecturally distinct systems: the same Orchestrator-Workers topology can implement Plan-and-Execute, Hierarchical Delegation, or Adversarial Verification -- three patterns with fundamentally different failure modes and design trade-offs. 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, Parallel, Orchestrate, Loop, Hierarchy). The resulting 7x6 matrix identifies 27 named patterns, 13 with original names. We demonstrate orthogonality through systematic cross-axis analysis, define eight representative patterns in detail, and validate descriptive coverage across four real-world domains (financial lending, legal due diligence, network operations, healthcare triage). Cross-domain analysis yields five empirical laws of pattern selection governing the relationship between environmental constraints (time pressure, action authority, failure cost asymmetry, volume) and architectural choices. The framework provides a principled, framework-neutral, and model-agnostic vocabulary for AI agent architecture design.

Problem

• AI agent design patterns
• cognitive function
• execution topology
• architectural classification
• design trade-offs

Innovation

• AI agent design patterns
• cognitive function
• execution topology
• two-dimensional framework
• architectural orthogonality