AI Agent Memory and History Management for Production

Building a conversational AI agent is straightforward in a prototype, but managing long-term chat history in production introduces complex challenges around token limits, latency, and context loss. To build reliable AI assistants, you must master the art of state and memory management. This text-only course guides you through the foundational concepts of LLM memory design, helping you transition from basic chat logs to sophisticated, production-ready history management strategies. You will learn how to keep your agents context-aware without exceeding model limits or inflating your API costs. What you'll learn: - Understand foundational memory concepts, including buffer memory, sliding windows, and token counting. - Implement truncation and summarization strategies to keep conversations relevant and cost-effective. - Explore database persistence layers to store and retrieve chat histories securely across sessions. - Apply vector database patterns to implement long-term semantic memory for complex AI agents. - Analyze the trade-offs between context window size, response latency, and API costs in production environments. Starting with key terminology and basic message structures, this course walks you through progressively advanced memory architectures. You will explore practical, written code examples and structural patterns that you can immediately apply to your own conversational applications. This course is designed for software developers and AI enthusiasts who understand basic programming and want to build smarter, more efficient LLM applications. No advanced machine learning background is required. Start reading today to build smarter, more context-aware AI agents that scale.