Definition
What is Context Compression?
Last updated
The practice of reducing token count in an AI agent's context window while preserving the information needed to complete tasks.
As AI agents work through multi-step tasks, they accumulate conversation history, tool outputs, and observations that dilute attention. Context compression techniques like structured summarization, tool response offloading, and embedding-based reduction keep the working context focused. Research shows effective compression can reduce memory usage by 26-54% while preserving task performance.
Further reading
Articles about Context Compression
When agent memory needs sleep, and when it doesn't
Memory consolidation fixes one specific failure: agents writing the same claim dozens of times into a flat scratchpad. When it helps and where it breaks.
Why your AI costs are a context problem
Token prices fell 280x but enterprise AI spend rose 320%. Poor context architecture drives 60-70% of total AI costs. Here is where the money actually goes.
Context compression: why less context means better AI
Context compression reduces AI agent memory usage by 26-54% while preserving task performance. Here's how it works and why bigger context windows aren't the answer.
Why AI agents forget mid-task (and how to fix it)
65% of agent failures come from context drift, not token limits. Here's how context compression keeps long-running AI agents on track.
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