Beyond Snippet Assistance: A Workflow-Centric Framework for End-to-End AI-Driven Code Generation

Recent AI-assisted coding tools, such as GitHub Copilot and Cursor, have enhanced developer productivity through real-time snippet suggestions. However, these tools primarily assist with isolated coding tasks and lack a structured approach to automating complex, multi-step software development workflows. This paper introduces a workflow-centric AI framework for end-to-end automation, from requirements gathering to code generation, validation, and integration, while maintaining developer oversight. Key innovations include automatic context discovery, which selects relevant codebase elements to improve LLM accuracy; a structured execution pipeline using Prompt Pipeline Language (PPL) for iterative code refinement; self-healing mechanisms that generate tests, detect errors, trigger rollbacks, and regenerate faulty code; and AI-assisted code merging, which preserves manual modifications while integrating AI-generated updates. These capabilities enable efficient automation of repetitive tasks, enforcement of coding standards, and streamlined development workflows. This approach lays the groundwork for AI-driven development that remains adaptable as LLM models advance, progressively reducing the need for human intervention while ensuring code reliability.