Key takeaways

• Prompt caching reuses computed state for an exact prompt prefix, at roughly a tenth the cost. It turns the quadratic cost curve back into a linear one.
• Cache hits need exact prefix matches, so put stable content first and variable content last, and never edit earlier messages mid-session; append new ones instead.
• Common cache-busters: changing tools, switching models, changing sandbox or working directory, editing AGENTS.md mid-run. Lock instructions before long sessions.
• Compaction keeps you under the context window by summarizing old turns (oldest tool outputs first), while preserving the original request and recent exchanges.
• Truncation (head plus tail) tames giant tool outputs, and context diffing avoids re-sending unchanged context. Compaction is lossy, so keep sessions focused.

Original sources: OpenAI's Unrolling the Codex agent loop, Anthropic's How Claude Code works, and the Deep Research Agents survey (arXiv:2506.18096).

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