Definition

What is Context Compression?

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.

Related concepts
Context Rot

The gradual degradation of an AI system's usefulness as the context it relies on becomes stale, incomplete, or outdated.

Context Window

The maximum amount of text (measured in tokens) that a language model can process in a single inference call.

Context Engineering

The practice of deliberately designing, structuring, and managing the information provided to AI models to improve output quality and relevance.

AI Agent

An autonomous software program that uses a large language model to plan and execute multi-step tasks.

Further reading
Why AI Agents Forget Mid-Task (and How to Fix It) How Prompt Caching Cuts AI Agent Costs by 90% Context Compression: Why Less Context Means Better AI Why Your AI Costs Are a Context Problem

All terms

View full glossary
AI Agent Context as a Service Context Container Structured Context Context Engineering Context Portability Context Rot Context Window MCP (Model Context Protocol) MCP Server RAG (Retrieval-Augmented Generation) Semantic Search Multi-Agent System Context Compression Context Drift Context Poisoning Prompt Caching Wire

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