Published on May 15, 2026
Current approaches to AI agent design often focus on either how data flows within a system or what tasks the agent performs. Industry guidelines from major companies like Anthropic and Google primarily emphasize execution topology. Meanwhile, cognitive science examinations delve into cognitive functions, leaving a gap in comprehensive evaluation.
A recent paper proposes a dual-axis framework that integrates cognitive functions and execution topologies. This new method categorizes seven cognitive functions against six structural archetypes, resulting in a robust 7×6 matrix. , it identifies 27 distinct patterns and provides clarity on how similar structures can serve different purposes.
Through cross-domain studies in finance, law, network operations, and healthcare, researchers have validated this framework. They define eight key patterns in detail and establish five empirical laws governing the choice of architectural designs. This analysis reveals how environmental constraints influence decision-making in agent architecture.
The proposed framework offers a clear, principled vocabulary for AI architecture, bridging gaps between disparate fields. This clarity could be transformative for developers, enhancing the design of more effective AI agents. As the AI landscape evolves, this tool may redefine best practices in agent design and implementation.
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