Since AI agents are becoming an inseparable part of various applications across financial, healthcare, customer service, and engineering domains, one issue remains at the forefront: how to keep models accurate, relevant, and aligned with the changing demands of users. Powerful standard pre-trained models usually fail to perform well in narrow tasks without a continuous tuning process. This has given impetus to Agile-based fine-tuning—a feedback-driven process in which AI agents are aligned through iterative, short cycles, similar to those used in agile software development (Tupsakhare, 2022). Such a strategy encourages constant change and step-by-step evolution, steered by actual user feedback loops.
Agile Meets AI: A Synergistic Framework
Agile practices focus on sprints, quick iterations, stakeholder comments, and unceasing delivery. This, together with the AI fine-tuning, becomes a dynamic process: gather user feedback, retrain or adjust the model, roll out the adjustments, repeat. An agile approach to AI systems could reduce time-to-market on model updates by 30% and maintain accuracy through a drift in the data (LinkedIn, 2024).