AutoGPT Devloop: An Autonomous AI Development Framework for End-to-End Software Generation, Execution, and Self-Repair

Abstract

We present the Autonomous AI Development Framework (AADF), a self-developing agent that converts high-level goals into functioning software through iterative planning, code generation, execution, and self-repair. AADF integrates (i) task decomposition, (ii) a semantic code memory (vector database), (iii) virtualized environment management, and (iv) automated file/version control to achieve autonomy across the software lifecycle. Using a Design Science Research approach, we evaluate AADF on a suite of 15 GUI / web tasks spanning five difficulty levels (3 trials each; 45 trials). The framework achieves 100% success on Levels 1-2 and Level 4, with partial degradations on tasks dependent on external API credentials or large NLP resources (Level 3: 67 % / 33 % on two tasks; Level 5: 67% on one task). Overall, AADF completes 41 / 45 trials (91.1 %) without manual code editing, demonstrating reliable self-repair for dependency conflicts and missing imports, and reducing human effort on environment setup, boilerplate, and repetitive file operations. We discuss error taxonomies (credentials, heavyweight downloads, concurrency), autonomy-speed trade-offs, and actionable design guidelines for safe deployment in practice.