Building AI Agents: Context Management and Message Summarization

Large language models have strict context limits, and long chat histories quickly become expensive and slow. To build production-ready AI systems, you must learn how to efficiently manage memory and summarize conversations on the fly. This text-only course guides you through designing and implementing an intelligent chat-history manager that automatically compresses old messages without losing crucial context. You will learn how to transition a simple prototype into a robust, production-ready AI agent. What you'll learn: - Understand the core concepts of LLM context windows, token limits, and memory management - Implement an automated summarization strategy to compress older messages when limits are reached - Design a clean, modular architecture to handle incoming user inputs and system prompts - Apply modern error-handling and API rate-limiting strategies for production stability - Track and evaluate the quality of agent-generated summaries through structured criteria Starting with fundamental definitions of tokens and memory states, you will progress through writing step-by-step logic to slice message histories, invoke summarization prompts, and integrate the system into a functional agent prototype. This course is designed for software developers and AI enthusiasts who are new to building agentic workflows and want to master context optimization. No prior AI engineering experience is required. Start reading today to build smarter, more cost-effective AI agents.