create-code-generation

You can automate an AI software development workflow by orchestrating the complete lifecycle from feature story to pull request. This coordinates planning, test-driven implementation, documentation, governance, and code review through specialized skills.

Test-driven development workflow orchestration handles pull request preparation by enforcing branch discipline, validating test coverage, updating artifacts, and verifying PR readiness before delivery. Human approval checkpoints are required for scope and plan changes.

Yes, you can enforce human approval checkpoints during an AI-SDLC workflow. The orchestration process gates scope approvals, plan changes, ticket updates, and external delivery actions to maintain development discipline.

The best way to coordinate governance decisions and documentation updates is through a structured end-to-end workflow. This approach applies ADR decisions and artifact updates alongside implementation and testing.

No specific dependencies are required to run a complete software development lifecycle workflow. The orchestration process relies on delegated skill execution and human checkpoints rather than external dependencies.

You should not use an orchestrated AI-SDLC workflow for simple software changes that bypass governance or testing requirements. The process enforces branch discipline, test coverage validation, and human approval checkpoints for complex feature delivery.