Agentic AI Architecture Patterns for Production Systems
This technical article from the Redis Blog explores the transition from simple LLM prompt-response systems to agentic AI architectures required for complex production environments. It defines agentic systems as autonomous entities capable of planning, reasoning, remembering, and executing actions through iterative loops, distinguishing them from static pipelines. The guide outlines four core components: planning modules, memory systems, tool-calling interfaces, and reflection loops. It details five major architecture patterns, starting with the single-agent model based on Reasoning and Acting (ReAct), which faces challenges like context overflow in long-running tasks. The text emphasizes that while agents offer autonomy for multi-step objectives, they introduce latency and cost, making them unsuitable for simple, structured data retrievals. The article serves as a practitioner's guide for developers building scalable AI applications, highlighting the critical role of the underlying data layer, specifically referencing Redis, in managing memory and state for these dynamic systems. It aims to help teams choose the right runtime pattern based on task complexity, coordination needs, and latency budgets.
Wire timeline
Agentic AI Architecture Patterns for Production Systems
This technical article from the Redis Blog explores the transition from simple LLM prompt-response systems to agentic AI architectures required for complex production environments. It defines agentic systems as autonomous entities capable of planning, reasoning, remembering, and executing actions through iterative loops, distinguishing them from static pipelines. The guide outlines four core components: planning modules, memory systems, tool-calling interfaces, and reflection loops. It details five major architecture patterns, starting with the single-agent model based on Reasoning and Acting (ReAct), which faces challenges like context overflow in long-running tasks. The text emphasizes that while agents offer autonomy for multi-step objectives, they introduce latency and cost, making them unsuitable for simple, structured data retrievals. The article serves as a practitioner's guide for developers building scalable AI applications, highlighting the critical role of the underlying data layer, specifically referencing Redis, in managing memory and state for these dynamic systems. It aims to help teams choose the right runtime pattern based on task complexity, coordination needs, and latency budgets.
Redis Blog