Your agent hit the context limit. Here's the playbook

In the blog post, Jim Allen Wallace addresses the issue of AI agents hitting their context limits, which is a constraint on the number of tokens a model can hold in its working memory, often leading to incomplete tasks. The article provides a playbook with six steps to manage and optimize the context window, which includes trimming unnecessary tool outputs, summarizing and compacting older conversation turns, and moving durable state out of the window into external storage. This approach helps maintain a high-signal context that the agent can reason with effectively. The guide also emphasizes the importance of just-in-time retrieval of relevant information, isolating context-heavy subtasks to manage token usage, and ensuring the external storage layer is efficient and cost-effective. Redis Iris is highlighted as a comprehensive solution that integrates memory, retrieval, and data management in one system, enabling agents to access fresh context efficiently without the burden of managing disparate storage systems.

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