Go to NeurIPS 2025 Workshop LAW homepageAgentic Design Patterns: A System-Theoretic Framework
Keywords: Agentic AI, Design Pattern, System Theory, Foundation Model
TL;DR: A collection of 12 agentic design patterns derived from a system-theoric framework and directly mapped to a comprehensive taxonomy of agentic challenges
Abstract: With the development of foundation model (FM), agentic AI systems are getting more attention, yet their inherent issues like hallucination and poor reasoning, coupled with the frequent ad-hoc nature of system design, lead to unreliable and brittle applications. Existing efforts to characterise agentic design patterns often lack a rigorous systems-theoretic foundation, resulting in high-level or convenience-based taxonomies that are difficult to implement. This paper addresses this gap by introducing a principled methodology for engineering robust AI agents. We propose two primary contributions: first, a novel system-theoretic framework that deconstructs an agentic AI system into five core, interacting functional subsystems: Reasoning \& World Model, Perception \& Grounding, Action Execution, Learning \& Adaptation, and Inter-Agent Communication. Second, derived from this architecture and directly mapped to a comprehensive taxonomy of agentic challenges, we present a collection of 12 agentic design patterns. These patterns — categorised as Foundational, Cognitive \& Decisional, Execution \& Interaction, and Adaptive \& Learning — offer reusable, structural solutions to recurring problems in agent design. The utility of the framework is demonstrated by a case study on the ReAct framework, showing how the proposed patterns can rectify systemic architectural deficiencies. This work provides a foundational language and a structured methodology to standardise agentic design communication among researchers and engineers, leading to more modular, understandable, and reliable autonomous systems.
Submission Type: Position/Review Paper (4-9 Pages)
Submission Number: 85
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