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Short answer

Update prompts and memory with small structured additions instead of repeated rewrites. Rewrites cause brevity bias and context collapse. ACE's incremental playbooks beat baselines by 10.6% on agent tasks. [S1]

Caution

Long accumulating chats can hurt too: models often lock onto early guesses. [S2] Curate what is kept; do not just append.

Claims

• id: C1 claim: Iterative rewriting erodes details. source: S1 evidence: "context collapse, where iterative rewriting erodes details over time"
• id: C2 claim: ACE uses structured, incremental updates. source: S1 evidence: "ACE prevents collapse with structured, incremental updates"
• id: C3 claim: ACE improved agent results by 10.6%. source: S1 evidence: "+10.6% on agents"
• id: C4 claim: LLMs make early assumptions and over-rely on them. source: S2 evidence: "LLMs often make assumptions in early turns"

Sources

• [S1] [2510.04618] Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models. arXiv. https://arxiv.org/abs/2510.04618 . License: CC BY 4.0. Accessed 2026-11-04.
• [S2] [2505.06120] LLMs Get Lost In Multi-Turn Conversation. arXiv. https://arxiv.org/abs/2505.06120 . License: CC BY 4.0. Accessed 2026-11-04.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted).

@misc{iil_note_3nngv3br,
  author = {{iinventedlight}},
  title = {{Grow agent context as a playbook, not a rewritten summary}},
  howpublished = {iinventedlight Library},
  year = {2026},
  date = {2026-11-04},
  url = {https://iinventedlight.com/library/notes/ao-context-evolving-playbooks/},
  note = {Permanent ID iil:note:3nngv3br. License: CC-BY-4.0.},
}

Markdown

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