Qualification: Zhenfeng Cao, Agentic Software: How AI Agents Are Restructuring the Software Paradigm (arXiv:2606.05608v2, 2026)

Record: kaal:position:2026-09-12-004 · 2026-09-12

Cao proposes evaluation frameworks that agents can use for self-correction. Kaal's position is that unstaked monitoring does not solve the verification problem, because AI that monitors AI inherits the flaws of the agents it oversees. A red status is therefore not a verdict. It opens a review, with the evidence attached, by reviewers who have something at stake.

Affirmed commentary position. This record extends a source-bound scholarly claim but is not a verbatim paper claim.

Holds when

Cao's paper is a position paper citing benchmark evidence, not an empirical test of these claims.
The position is Kaal's own argument in the essay Prompts Don't Govern Agents. Reputation Does.
The position does not validate Cao's figures, benchmark methodology, or reported benchmark results.
The position is limited to AI evaluation, red-status review, and reviewer incentives.

Current debate

Zhenfeng Cao, Agentic Software: How AI Agents Are Restructuring the Software Paradigm (arXiv:2606.05608v2, 2026)

Scholarly basis

kaal:claim:5245185-027
Wulf A. Kaal, How can we Best Monitor AI Agents (2025). SSRN: https://ssrn.com/abstract=5245185
Source PDF sha256: 4d7adba83ec722480e97bde6528cbe9ce98c709e45cb18794f157a64b8fe7da2

Topics

ai-and-agents compliance

Provenance

Affirmed in kaalvis:2026-09-12:prompts-reputation-essay-six-v1 on 2026-09-12. Review record.

Verify

Canonical markdown sha256: 80a5f0aa9bc1af7441c1fa7becb4097919e58e5378fbe1ac2f6c91b232c06cb1
curl -s https://wulfkaal.github.io/positions/2026-09-12-004.md | sha256sum