Lesson 1744 of 2116
Context Compaction: How AI Agents Survive Long Sessions
Compaction strategies — summarization, eviction, and offloading — let agents work past their context limits productively.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2context compaction
- 3agent memory
- 4summarization
Concept cluster
Terms to connect while reading
Section 1
The premise
Long-running AI agents inevitably outgrow their context window. Compaction strategies — recursive summarization, episodic eviction, file-based offload — keep them productive past the wall.
What AI does well here
- Recursively summarize older turns to preserve narrative
- Evict tool-call noise while preserving outcomes and decisions
- Offload artifacts to files and re-load by reference
What AI cannot do
- Avoid losing some information at every compaction step
- Substitute for genuine long-context model capability
- Recover details the agent never explicitly recorded
Key terms in this lesson
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