Creative Industry Group · AI Harness Industry Enablement Overview

1. Introduction

1.1 Positioning and Scope of the Group

The Creative Industry Group covers six directions: Smart Manufacturing, Industrial Internet, Media, Creative Design, AI Drama, and AI Animation. These six directions may seem to belong to two entirely different domains — "material production" and "content production" — but from the AI Harness perspective they share one common engineering thread: converting the uncertainty of model output into deliverables that are orchestratable, observable, constrainable, and verifiable through regression.

This group's division is based not on industry classification, but on the difference in adjudicability of the deliverables:

• For Smart Manufacturing and Industrial Internet, the deliverables are state changes in the physical world (a production scheduling run, a quality-inspection decision, a piece of equipment maintenance), and the judging criteria are physical quantities such as yield, miss rate, and downtime. The cost of errors is directly reflected as waste and safety incidents, so the L5 evaluation and observation layer and the L6 governance and safety layer are the deployment bottleneck.
• For Media, Creative Design, AI Drama, and AI Animation, the deliverables are content assets (an article, an image, a video), and a large part of the judging criteria concerns "whether it is still the same person, the same brand, the same tone." This is a class of cross-session, cross-shot, cross-episode state-keeping problems, so the L4 memory and state layer is the quality bottleneck.

This judgment is the common premise of all documents in this group. See 4. Six Directions × Harness Six-Layer Capability Mapping.

图 1-1｜AI Harness 六层体系与六方向瓶颈映射

数据来源：基于本文分析绘制的示意图。

1.2 Why the Creative Industry Needs Harness

First, improvements in model capability do not automatically translate into improvements in delivery capability. In Coca-Cola's 2025 holiday campaign, 5 AI experts spent 30 days fine-tuning more than 70,000 video clips (per behind-the-scenes reports, not officially disclosed) before the generative video model reached a broadcastable standard; the 2024 version, by contrast, drew widespread criticism for distorted figures. This shows a huge engineering gap between the "clip quality" of generative model output and the "final-film quality," and it is precisely this gap that Harness fills.

Second, compliance obligations have already been pushed down to engineering details. The "Measures for the Labeling of AI-Generated and Synthesized Content" (National Cyberspace Administration Protocol No. [2025] 2) took effect on September 1, 2025, requiring generated and synthesized content to carry explicit and implicit labels; Article 9 requires relevant logs to be retained for no less than six months; Article 10 specifies that "no organization or individual may maliciously delete, alter, forge, or conceal the labels for generated and synthesized content specified in these Measures, nor provide tools or services for others to carry out the above malicious acts." This means that "writing metadata," "adding corner marks," and "keeping logs" are no longer operational tasks, but rather the L2 tool contract and the L6 governance red line for Harness.

Third, scale has already crossed the inflection point where manual fallback is feasible. According to the China Netcasting Services Association's "Micro-Drama Creation Guide" (cited from People's Daily, 2026), in Q1 2026 the industry launched about 128,000 micro-dramas, of which about 122,000 were AI micro-dramas, accounting for over 95%. At this scale, any quality system relying on manual item-by-item review will fail; consistency checks, labeling checks, and pre-review must be turned into automated gates on the production line.

1.3 Manifestation of the Three-Generation Architecture Evolution in this Group

A key engineering principle of the third generation comes from Anthropic's explanation of Agent Skills: having a model generate sorted results token by token is far more expensive than directly running a sorting algorithm, and many applications need "the deterministic reliability that only code can provide." For the Creative Industry Group, operations such as frame extraction, color correction, metadata writing, label verification, and print-file export should be wrapped as scripts rather than handed to the model. This principle runs through all the AGENTS.md and SKILL.md files in this group.

2. Glossary

3. Positioning Comparison of the Group's Six Directions

4. Six Directions × Harness Six-Layer Capability Mapping

Note: The specific implementation path of each direction in the table below is an engineering derivation based on retrieved materials; all values appearing in the table still need to be traced back to the original sources of the corresponding direction documents.

5. Common Challenges and Countermeasures

6. Document Navigation for this Group

The hierarchical relationship between AGENTS.md and SKILL.md follows the official specification: AGENTS.md has no required fields, is plain standard Markdown, the closest one to the file being edited takes effect, and it can be nested — each of this group's six directions holds its own nested AGENTS.md; group-level AGENTS.md defines common constraints while direction-level AGENTS.md defines dedicated toolchains; in the event of conflict, the closest one prevails, and explicit user instructions have the highest priority. SKILL.md carries task-oriented workflows (e.g. "a compliant AIGC material delivery"), written to the specification of a name ≤ 64 characters, a description ≤ 1024 characters, and a body < 5000 tokens and < 500 lines, while following the three-level load of progressive disclosure.

7. Summary

The six directions of the Creative Industry Group can be summarized into two main threads within the AI Harness system: on the content side, the L4 memory and state layer is the quality bottleneck; on the industrial side, the L5 evaluation and observation layer and the L6 governance and safety layer are the deployment bottleneck. This difference determines that the two sides have different engineering priorities — the content side must build asset libraries and consistency-check gates, while the industrial side must build an indicator system, data-sovereignty boundaries, and audit trails.

What the two sides share is the same set of L6 baselines: the "Measures for the Labeling of AI-Generated and Synthesized Content" took effect on September 1, 2025; explicit and implicit labels, de-labeling log retention of no less than six months, and the prohibition on deleting/altering/forging labels have already pushed compliance obligations down to the level of metadata fields and export scripts. Any Harness design in a creative-industry direction that cannot answer "where is this content's label written, who writes it, and how is it verified afterward" is not yet ready for delivery.

It should be stressed that much of the effect data cited in this group comes from vendor case pages, broker-research compilations, third-party estimates, and self-media retrospectives, with varying levels of credibility. This document uniformly uses phrasings such as "as disclosed by the specific disclosing party (company or institution name)," "third-party estimate," and "non-official disclosure" to annotate such data rather than elevating it to assertions, and sets up an information-gap declaration at the end of each direction document.

Information Gap Declaration

8. References

1. Measures for the Labeling of AI-Generated and Synthesized Content — Cyberspace Administration of China, Ministry of Industry and Information Technology, Ministry of Public Security, National Radio and Television Administration, 2025. https://www.cac.gov.cn/2025-03/14/c_1743654684782215.htm
2. Promoting the Construction of the Labeling System through Multiple Measures to Support the Healthy Development of AI in the New Era — National Internet Emergency Center, 2025. https://www.cac.gov.cn/2025-09/06/c_1758880709361356.htm
3. "Measures for the Labeling of AI-Generated and Synthesized Content" Helps Identify False Information — Chinese Government Website, 2025. https://www.gov.cn/zhengce/202503/content_7014404.htm
4. From September 1, AI-Generated and Synthesized Content Must Carry Labels — CCTV, 2025. https://big5.cctv.com/gate/big5/news.cctv.cn/2025/09/01/ARTI3ZlXK7MyM39Pm3PuZ5Hm250901.shtml
5. Embracing the Intelligent Wave and Swimming Toward the Depths of Change — Xinhua News Agency, 2025. https://www.news.cn/20251114/1c01598d836c449bbfd54267b6ecea6d/c.html
6. Research Report on New Media Run by Mainstream Media (2024–2025) — People's Daily Online, 2025. https://sc.people.com.cn/BIG5/n2/2025/1030/c345167-41396739.html
7. Will AI "Shuffle the Deck" for Micro-Dramas? — People's Daily, 2026. https://kpzg.people.com.cn/n1/2026/0511/c404214-40717117.html
8. AGENTS.md official site — Agentic AI Foundation (Linux Foundation), 2025—2026. https://agents.md/
9. Lesson 15: AGENTS.md - giving agents project context — Addy Osmani, 2026. https://addyosmani.com/agents/15-agents-md/
10. AGENTS.md Boilerplate (writing principles) — Sentry developer documentation. https://develop.sentry.dev/sdk/getting-started/templates/agents-md
11. Agent Skills Specification — agentskills.io, 2025—2026. https://agentskills.io/specification
12. Equipping agents for the real world with Agent Skills — Anthropic, 2025 (updated to an open standard on 2025-12-18). https://claude.com/blog/equipping-agents-for-the-real-world-with-agent-skills
13. Building custom Skills for Claude — Anthropic Claude Cookbook, 2025. https://platform.claude.com/cookbook/skills-notebooks-03-skills-custom-development
14. Introduction to the Assessment of Smart Manufacturing Capability Maturity (GB/T 39116-2020, GB/T 39117-2020) — China Electronics Standardization Institute. https://www.cc.cesi.cn/service/show-2478.aspx
15. ISO 23247-1:2021 Digital twin framework for manufacturing — ISO. https://www.iso.org/standard/75066.html
16. Coca-Cola Refreshes Givers of the Season — The Coca-Cola Company, 2025. https://coca-colacompany.com/media-center/coca-cola-refreshes-givers-of-the-season-embraces-ai-powered-storytelling-in-global-holiday-campaign