What belongs in an agent's context window (and what to evict)
Article summary
Quick briefing — cleaned from the original RSS feed
Most "agent memory" writeups are about getting things in: which vector store, how to chunk, how to embed. The harder question in practice is the opposite one. Your context window is a fixed budget. Every token you spend on a stale tool output is a token you did not spend on the thing that actually decides the next step. So the real engineering problem is not retrieval, it is eviction: what earns a place in context, and what gets dropped or compressed. Here is the model that has held up for me…
1Key Takeaways
- Most "agent memory" writeups are about getting things in: which vector store, how to chunk, how to embed.
- The harder question in practice is the opposite one.
- Your context window is a fixed budget.
- Every token you spend on a stale tool output is a token you did not spend on the thing that actually decides the next step.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that most "agent memory" writeups are about getting things in: which vector store, how to chunk, how to embed.
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