AI Agents: Managing Token Limits and Context Windows

Unlock the full potential of your AI agents by mastering how they process and retain information within the constraints of model limitations. This course will equip you with the foundational knowledge and practical strategies to design AI agents that intelligently manage their working memory, ensuring they can handle complex interactions and extended conversations without losing critical context or encountering token limit errors. What you'll learn: * Understand the fundamental concepts of Large Language Model (LLM) tokenization and counting * Apply strategies for effective context window management within AI agent architectures * Learn to implement compaction techniques, including recursive methods, to summarize and condense agent memory * Identify and mitigate potential data loss scenarios when managing AI agent context * Explore basic prompt engineering patterns to optimize context usage and agent performance * Understand the role of external knowledge retrieval (RAG patterns) in extending an agent's effective context The course begins with an exploration of tokenization fundamentals and progresses through practical techniques for context management, culminating in advanced strategies for memory compaction and external data integration. This course is designed for beginners with an interest in developing AI agents, requiring no prior experience with token management or advanced LLM concepts. Start building more intelligent and reliable AI agents today.