AI Code Generation from Requirements: Shipping via GitHub Pull Requests You Can Review

Coding assistants autocomplete the line you're typing. Requirement-driven code generation starts three steps earlier: with a user story, its acceptance criteria, the approved design, and your repository's conventions — and ends with a pull request a human reviews. The difference in leverage is enormous, and so is the difference in required guardrails.

The unit of work is a story, not a prompt

In VrittOS you don't prompt for code. You approve a backlog, then start implementation on a story. The developer agent receives the full context chain: the story and its acceptance criteria, the technical foundation the tech-lead agent planned, the design system and mockups, and your organisation's engineering standards. Prompting is the platform's job; reviewing is yours.

Why GitHub is wired in, not bolted on

Code generation without source control is a demo. VrittOS requires a connected GitHub repository before any code is generated, because the guardrails live there:

• Feature branches only — every story gets its own branch (ai/<project>/<story>).
• Never a direct push to main — output is always a pull request.
• Conventional commits and a PR body that lists the summary, files changed, tests added, test results, and known limitations.
• Your CI runs on the PR — generated code faces the same gate as human code.

Standards make generated code yours

Uncontrolled generation produces a codebase with five architectural opinions. VrittOS applies configured engineering standards — stack preferences, project structure, naming, testing conventions — so the tenth generated PR looks like it came from the same team as the first. Architecture context accumulates per project: agents analyse the repository before they write to it.

Tests ride in the same PR

A QA agent generates unit and integration tests alongside the implementation, and the results are posted in the PR body. If CI fails after merge-time changes, the self-healing loop reads the failure and pushes a fix commit to the same branch.

What review looks like in practice

Reviewing an AI pull request is faster than reviewing a human one, for an unexpected reason: the PR explains itself. The story link says why, the PR body says what, the acceptance criteria say done, and the test summary says proven. Your job narrows to judgement calls: is this the right abstraction, did it miss a domain rule, should this be two PRs?

Honest limits

Requirement-driven generation is strongest on well-scoped product features — CRUD flows, integrations, UI from mockups, API endpoints with clear contracts. It is weaker on gnarly performance work, ambiguous migrations, and code with undocumented tribal knowledge. That's why every PR stops for review, and why humans stay in the loop by design.

See a story become a PR: start a free 14-day trial, connect a GitHub repository, and take one story from your backlog to a reviewable pull request. From £29/month.