Designing Agentic AI Systems, Part 1: Agent Architectures

Building a robust agentic system involves breaking it down into three crucial layers: tools, reasoning, and action, each with its own set of challenges that can affect the system's overall performance. The tool layer is responsible for interfacing with external data sources and ensuring the retrieval of high-quality data, the reasoning layer processes this data using a large language model (LLM) to guide actions, and the action layer orchestrates interactions between the system and the external world. Effective design requires understanding how these layers interact to build reliable, predictable, and resilient systems. The roadmap for developing such systems includes modularizing tasks to avoid monolithic designs, ensuring clear interactions between sub-agents, integrating fresh and relevant data through retrieval-augmented generation (RAG), and addressing cross-cutting concerns like observability, performance, and security. This comprehensive approach aims to equip developers with the tools to create agentic systems that can withstand real-world production demands.