Dev agencies have shifted AI coding tools from experimental to production-standard. Most now use a three-layer stack: chat assistants (Claude, ChatGPT) for planning and architecture, AI-native editors (Cursor) for building features across codebases, and inline assistants (GitHub Copilot) for boilerplate. The real-world workflow keeps humans in the loop — engineers break work into small tasks, review every AI-generated line, and run code through normal pull request processes. AI has compressed MVP timelines from 4–6 months to 6–12 weeks and improved test coverage, but token costs for heavy agentic use are a new expense. A 'vibecode rescue' niche has emerged for fixing AI-built apps that hit stability walls. The agencies that succeed are those with senior engineers who know when to trust the tools and when to override them.
