Inside an AI-assisted development workflow: Claude Code, Cursor, and human review.

Why does the workflow matter more than the tools?

Anyone can generate code in 2026. The difference between a demo and a product is the process wrapped around the generation. Our workflow (the same one on our homepage) has seven steps: three belong mostly to humans, two mostly to AI, and two are genuinely shared.

The seven steps

1. Idea & scope (human). A build plan with features, priorities, and a timeline. The single highest-leverage hour in the project.
2. UI/UX direction (human). Wireframes and a design system in Figma before code. AI builds much better when the target is visual, not verbal.
3. AI-assisted development (AI-heavy). Claude Code handles multi-file features, refactors, and test runs from the terminal. Cursor handles fast inline work. Trae picks up builder-style tasks. Output lands as pull requests, never straight to main.
4. Human code review (human). A senior engineer reviews architecture fit, security, and edge cases on every PR. This is the step that makes vibe coding production-safe,full explainer here.
5. Testing & QA (shared). Playwright suites run in CI; a human runs the real flows a test can't feel: onboarding friction, weird devices, empty states.
6. Deployment (shared). CI/CD to Vercel or Netlify, staging first, monitoring on, rollback ready. Launches should be anticlimactic.
7. Scaling & maintenance (human-led). Performance passes, iteration on user feedback, and new features, with AI doing the heavy lifting inside the same guardrails.

What does the AI actually do, and not do?

AI is excellent at:

• Scaffolding apps, components, and API routes
• Multi-file refactors that would eat a human afternoon
• Writing the first draft of tests
• Integrations against well-documented APIs (Stripe, Supabase)
• Chasing down its own type errors and lint failures

Humans stay in charge of:

• Architecture and data-model decisions
• What to build at all (and what to cut)
• Security review and authorization logic
• Judging when code is maintainable, not just working
• The final call before anything reaches production

Which failure modes does this prevent?

• The 80% demo that never ships: killed by scoping and deployment discipline.
• Architecture spaghetti: killed by human-owned structure and review.
• Silent security holes: killed by the explicit security pass.
• "It worked yesterday": killed by CI, tests, and staging.
• Vendor hostage situations: killed by working in your repo from day one.

What does this mean for cost and speed?

The build phase compresses 3–4×, which is why AI-assisted teams quote 30–60% below traditional agencies for the same scope (numbers in our MVP cost guide). The review, testing, and deployment steps don't compress much, and that's deliberate. They're the insurance policy on everything the AI just wrote.

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