AI Agent Context Limit: 6-Step Recovery Playbook

This post provides a practical playbook for recovering AI agents that hit context limits. It outlines six steps: 1. Trim tool outputs (hard token caps, pagination, dynamic filtering, referencing instead of inlining). 2. Summarize and compact older turns to reduce token count and correct errors, while noting the lossy nature and suggesting external storage for full history. 3. Move durable state out of the context window into external storage (like Redis) as the standard architecture for production agents. 4. Implement on-demand retrieval (JIT) instead of carrying the full history, using RAG pipelines and best practices like budgeting the window and checking relevance. 5. Isolate context-heavy subtasks into scoped subagents running in their own windows. 6. (Implied) Use a fast external store like Redis Iris for efficient retrieval and state management.