Context Pruning for AI Agents: Long Sessions Without Overflow
Long-lived LLM-based AI agents run in an endless loop and quickly accumulate a long context history. When the context overflows, the model starts to degrade. Context pruning is a new technique that automatically removes old and unneeded information while keeping critical data. This lets agents work for hours without losing quality.
AI-processed from Machine Learning Mastery; edited by Hamidun News
AI agents are growing more complex and longer-lived, but they face a serious problem: the context fills up quickly during long-running tasks.
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