Context Engineering Explained: Mechanisms for Deciding When to Compress Context

Previous Articles:

• How AI Agents Work: The OpenClaw Case
• Context Engineering Explained: A Core Technology Behind AI Agents （LLM Summary, Observation Masking & Memory）
• Context Engineering Explained: What to Do When Tasks an AI Agent Could Originally Complete Fail After Context Engineering

Contents:

1. The Model Itself May Not Be Inclined to Compress Context Proactively
2. Teaching the Model to Compress Context Proactively
3. Calling a Subagent Can Also Be Viewed as a Form of Proactive Context Compression

1. The Model Itself May Not Be Inclined to Compress Context Proactively

As we already know, the more we interact with a model, the longer the context becomes. When necessary, we therefore need to compress the context and reduce its length.

For the model itself, “compressing” context is, to put it rather starkly, essentially a matter of “erasing” details from its own past experience.

In practice, however, the model itself may not be willing to do this proactively.