AI agent architecture: patterns, components, and how to build for web access

AI agent architecture is the structural design that determines how autonomous systems perceive, reason, plan, act, and learn, impacting their ability to complete complex tasks or fail under real-world conditions. The architecture involves several core components including a foundation model for reasoning, memory systems for context management, planning modules for goal decomposition, tool interfaces for real-world actions, and a retrieval layer for accessing current information. There are four primary architecture patterns—reactive, deliberative, hybrid, and multi-agent—each suited for different levels of task complexity. The retrieval layer is crucial for ensuring agents provide accurate, verifiable information, while the agent harness manages context, tool orchestration, and error handling. Successful deployment from prototype to production requires considerations of logging, human-in-the-loop controls, verifiable data sources, and cost management to achieve scalable and reliable AI systems. Understanding and implementing these architectural components and patterns are essential for developing production-ready AI agents that are robust, efficient, and capable of handling sophisticated tasks.

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