AI agent architecture: Build systems that actually work

AI agent architecture is an advanced framework for designing autonomous systems that adapt to changing environments and pursue goals with minimal human intervention. Unlike traditional AI systems, agent architectures incorporate components such as perception and input processing, reasoning engines, memory systems, tool execution, and orchestration to enable complex decision-making and task execution. These systems can maintain context, learn from experience, and integrate external tools to achieve objectives. Various architectural patterns, including ReAct, Plan-and-Execute, and multi-agent systems, cater to different constraints like latency, cost, and reliability. Memory and retrieval systems play a crucial role in maintaining context and enhancing efficiency, with technologies like Redis providing unified infrastructure for real-time data handling. The architecture must also consider real-world constraints such as reliability, integration complexity, latency, cost control, and observability, ensuring robust and scalable AI solutions.