Managing context windows is a key challenge in AI agent development. Unlike chatbots, agents must dynamically allocate limited context to goals, rules, tools, memory, and history. This article explores four strategies: fixed retention for critical data, recent retention for immediate tasks, compression summaries for long-term memory, and on-demand loading for large datasets. Each approach balances information completeness with model efficiency. For developers, mastering these techniques is essential for building reliable, long-running agents. The article provides clear examples and trade-offs, making it a valuable reference for production systems.
A practical guide to context management strategies for AI agents, including fixed retention, compression, and on-demand loading.