Agentic Design Patterns: A System-Theoretic Framework
🤖 AI Summary
Current agent system designs often lack grounding in systems theory, resulting in ad hoc architectures prone to hallucination and reasoning flaws that undermine reliability. This work addresses this gap by introducing systems theory into agent architecture design for the first time, proposing a structured framework composed of five core functional subsystems. Building on this foundation, the authors abstract twelve reusable and clearly categorized agent design patterns. Through the reconstruction and validation of representative frameworks such as ReAct, the proposed approach effectively rectifies inherent architectural deficiencies, significantly enhancing modularity, interpretability, and reliability. This contribution establishes a standardized language and a structured development paradigm for agent engineering, offering a principled foundation for future research and practice.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
agentic AI
design patterns
system-theoretic framework
hallucination
reasoning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic Design Patterns
System-Theoretic Framework
Foundation Models
Modular AI Architecture
Autonomous Systems Engineering
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