Agent ResearchDeepSeek
Evidence check
  • Preprint
  • Code/data available
  • Not independently replicated

Coding Agents Can’t Build the Agent You Actually Want — Yet

AgentSWE's 25 commission-style tasks: strongest builders average 19.1 on Creation and 31.6 on Editing. Optimization closes under 5% of the remaining gap; 58.1% of held-out Creation runs fail validity gates.

Christopher Ort
By Christopher Ort · 2 min read
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Coding agent attempting to build another AI agent, resulting in an incomplete or flawed construction.
Illustration: i10X
On this page
  1. Quick Take
  2. What the study shows
  3. Why it matters: commissions are not exams
  4. Method in brief
  5. What remains open
  6. Context
  7. What it means for teams commissioning agents
  8. About the analysis
  9. i10x Perspective
i10X Quick Take

Quick Take

  1. The finding: Across Creation, Editing and Optimization of agents, no coding-agent builder comes close to delivering what a requirement asks for.
  2. The demo–delivery gap: In Optimization, candidates gain 13.1 points on development cases but only 1.8 on held-out cases.
  3. Hard failures: In Creation, 58.1% of traced held-out executions fail a validity gate — usually by erroring or timing out.

Coding agents are increasingly asked to build other agents for a user’s workflow. Beijing Academy of Artificial Intelligence and collaborators introduce AgentSWE to test whether the delivered agent actually meets the requirement — not whether it demos well on a few examples.

Column chart: strongest Creation average 19.1, Editing 31.6, Optimization closes at most 5 percent of the remaining gap.
i10X chart based on data from Wang et al. (2026), arXiv:2610.13361, main comparison (CC BY 4.0).

What the study shows

25 tasks across Creation (agent from a natural-language requirement), Editing (production agent codebases) and Optimization (toward a measurable target). Builders iterate on illustrative cases; acceptance runs the frozen submission on held-out cases with requirement-derived rubrics.

  • Low averages: Strongest builders average 19.1 on Creation and 31.6 on Editing.
  • Optimization barely moves the needle: No builder closes more than 5% of the remaining gap to the target on average; only 13 of 25 runs beat their starter.
  • Overfitting playbooks: On PinchBench, DeepSeek-V4-Flash and Pro gained 8.1 and 59.4 on development cases via keyword playbooks, then lost 15.2 and 15.1 on held-out cases.
  • Cases matter: Going from zero to two development cases raises mean Result by 34.3 points on PDF translation and 29.3 on PPTX generation; without any case, all three PDF deliveries score zero.

Why it matters: commissions are not exams

Passing a few demo cases is how agents get greenlit. AgentSWE shows that pattern systematically overstates delivery.

Method in brief

Held-out acceptance with hard pass/fail gates or source-benchmark objectives for Optimization.

What remains open

  • 25 tasks: Early benchmark; coverage will grow.
  • Builder set: Results reflect current coding agents and harnesses.

Context

Related coding-agent evals in Agents.

What it means for teams commissioning agents

  • Hold out acceptance cases: Never grade only on what the builder saw.
  • Require validity gates: Many submissions never even run.
  • Watch keyword playbooks: Huge train gains can reverse on held-out work.

About the analysis

Researched and written with AI assistance under the editorial responsibility of the i10X News team. The title image is an AI-generated illustration. Read our AI Use Policy. Spotted an error? Request a correction.

This article is based on the full text of the preprint “AgentSWE: Can Coding Agents Build the Agent You Actually Want?” (arXiv:2610.13361v1, submitted October 9, 2026). All numbers are taken from the paper’s text, tables and figures; i10X did not rerun the experiments. The chart was redrawn by i10X from main comparison of the paper, which is published under a CC BY 4.0 license.

i10x Perspective

AgentSWE’s core claim is almost moral: a commission is a promise about unseen work. Today’s coding agents still deliver demos. Until Creation clears validity gates and Optimization generalizes, “build me an agent” remains an aspirational prompt.

Christopher Ort

Christopher Ort

Christopher Ort handles SEO, AI agent workflows and PR & partnerships at i10x.ai, the all-in-one AI workspace built by i10X Global Pte. Ltd. in Singapore. He is a graduate of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the author of “The Style Penalty”, a study published on SSRN that analyses 1,576 evaluation decisions by four frontier AI models to show how AI-written CVs fare in automated hiring screens; the research has been covered by heise online and t3n.

Timeline

What happened before

  1. Release

    DeepSeek-V4.1-Flash

    1M context · $0.3 / $1.2 per 1M tokens

    api-docs.deepseek.com (source, opens in a new tab)
  2. Release

    DeepSeek-V4-Pro (GA)

    1M context · $1.32 / $3.96 per 1M tokens

    api-docs.deepseek.com (source, opens in a new tab)
  3. Release

    DeepSeek-V4-Flash

    api-docs.deepseek.com (source, opens in a new tab)
  4. Funding

    DeepSeek closes first external round of over $7B

    First external round · ¥50B+ at $50B valuation

    Per The Information via Reuters: >¥50B (~$7.4B) at a post-money valuation above $50B, with an unusual deal structure.

    reuters.com (source, opens in a new tab)
  5. Release

    DeepSeek-V4 (Pro & Flash preview)

    1M context · open weights

    V4-Pro and V4-Flash preview with a million-token context; legacy deepseek-chat/reasoner retired.

    deepseek.com (source, opens in a new tab)
Full DeepSeek timeline ›

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