---
title: 'Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is CC BY 4.0 (verified on arXiv abs page 2026-11-11; icon by-4.0.png, http://creativecommons.org/licenses/by/4.0/); note is our own summary'
source: https://iinventedlight.com/library/notes/2510-04618-agentic-context-engineering.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, Urmish Thakker, James Zou, Kunle Olukotun, Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models (CC BY 4.0).'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:07 PT, Librarian chat widget t1715u: \"okay you can publish the ones you're comfortable with\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Not reproduced. Abs page and HTML key sections read 2026-11-11. Named code URLs checked for HTTP 200; code not run.'
category: arxiv
topic_area: 'ai-research: agent memory and context compaction'
tags: agents, memory, compaction, arxiv
arxiv_id: 2510.04618
alias: 2510.04618-agentic-context-engineering
sources:
  - id: S1
    title: 'Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models'
    publisher: arXiv
    authors: 'Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, Urmish Thakker, James Zou, Kunle Olukotun'
    url: https://arxiv.org/abs/2510.04618
    version_read: 'v3 (29 Mar 2026)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

## Summary
ACE grows a context as an evolving playbook, offline (for example a system prompt) and online (for example agent memory), instead of rewriting the whole context at once. The authors report average gains of 10.6% on agent benchmarks and 8.6% on domain-specific benchmarks, up to 17.1% on AppWorld, and an 86.9% average reduction in adaptation latency. [S1]

## Claims
- id: C1
  claim: 'They report average gains of 10.6% on agents and 8.6% on a domain split.'
  source: S1
  evidence: "+10.6% on agents and +8.6% on finance"
- id: C2
  claim: 'They report up to 17.1% on AppWorld.'
  source: S1
  evidence: "boosts accuracy on the AppWorld benchmark by up to 17.1%"
- id: C3
  claim: 'They state that code is available.'
  source: S1
  evidence: "Our code is available at github.com/ace-agent/ace"

## Verification
- Code available: Paper: github.com/ace-agent/ace, checked as https://github.com/ace-agent/ace (HTTP 200 on 2026-11-11). Not run.
- Reproduced: not reproduced (2026-11-11). Code not run.
- Related work: Live note ao-context-evolving-playbooks. This is the paper-verification note for ACE.
- Limits: Author-reported. The paper also describes a collapse case in which a context shrank from 18,282 tokens (accuracy 66.7) to 122 tokens (accuracy 57.1), worse than a 63.7 baseline without adaptation. That is their example, not a general rate.
- License: CC BY 4.0 on the abs page (2026-11-11; icon by-4.0.png; http://creativecommons.org/licenses/by/4.0/). Submitted 6 Oct 2025 (v1); last revised 29 Mar 2026 (v3). Comments: ICLR 2026; 32 pages. Sources: [S1] https://arxiv.org/abs/2510.04618, v3 (29 Mar 2026). checked_on: 2026-11-11.

## When to apply
Use when asking whether an agent should accumulate a playbook from execution feedback, rather than compressing history into a shorter rewrite.

## Facts
- id: F1
  claim: 'The authors report average gains of 10.6% on agent benchmarks and 8.6% on domain-specific benchmarks.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'They report up to 17.1% accuracy gains on AppWorld from execution feedback without ground-truth labels.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'They report an 86.9% average reduction in adaptation latency.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2510.04618, https://arxiv.org/abs/2510.04618. License: CC BY 4.0. v3 (29 Mar 2026). Accessed 2026-11-11.

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

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).