A recent comprehensive analysis has cataloged 17 distinct architecture patterns for AI agents, providing a structured evaluation framework across six key dimensions: reasoning quality, scalability, maintainability, adaptability, efficiency, and robustness. The study introduces an industry-oriented seven-layer classification (ETCLSVG) that spans from environment interaction to governance, offering a holistic view of agent system design. This taxonomy is particularly valuable for engineers and architects building production-grade agent systems, as it helps in selecting appropriate patterns based on specific use cases and constraints. The analysis also highlights trade-offs between different architectural choices, such as the balance between reasoning depth and response latency. As agent-based AI systems become more prevalent in enterprise applications, having a standardized vocabulary and evaluation criteria becomes crucial for effective communication and design decisions. This work serves as a practical reference for both newcomers and experienced practitioners looking to deepen their understanding of agent architectures.
This article systematically analyzes 17 agent architecture patterns, evaluating them across six dimensions like reasoning quality and scalability. It introduces a seven-layer classification from an industry perspective, making it a valuable reference for designing robust AI agents. The framework is timely as agent-based systems gain traction in production.