Designing AI Agents: Context Window and Token Management Fundamentals

As AI agents engage in longer, more complex conversations, managing the context window becomes a critical challenge to prevent execution errors and control API costs. This course guides you through the foundational concepts of token limits, tracking context usage, and optimizing your agent's memory. You will transition from writing simple scripts to designing robust, state-aware AI agents that intelligently manage their own conversational history. What you'll learn: - Understand the fundamentals of tokenization and how large language models process context. - Track token usage dynamically within an active agent run loop. - Apply conversation compacting and summarization strategies when thresholds are reached. - Implement sliding window techniques to maintain relevant history without exceeding limits. - Configure fallback mechanisms for handling unexpected token overflows. You will start with core definitions and token calculations before moving into step-by-step code structures for run loops and memory management. The material focuses on clear written explanations and practical code snippets that you can adapt to any modern LLM framework. This course is designed for beginner AI developers and programmers transitioning to agentic workflows, with no prior AI agent experience required. Start reading today to master the essentials of efficient AI memory architecture.