Agile-Based Fine-Tuning of AI Agents for Domain-Specific User Feedback Loops

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). 

Core Components

The most important elements of agile-based fine-tuning are the data-based sprint planning, where the metrics such as the data drift scores and the user-reported matters lead and encourage each cycle of development. The feature flags and canary releases allow an organization to implement changes in steps and expose them to small groups of users so that performance can be proactively monitored before a full release (Pyke, 2025). Another important factor is the crossover feedback, where the support department, marketers, domain and data scientists would provide feedback. This allows the AI model to meet not only technical excellence but also business goals, which translates to more pertinent and accurate work in and by AI agents, respectively.

Domain-Specific Fine-Tuning

Recent studies highlight the effect of domain-specific fine-tuning:

Finance Bench (SEC filings) – A paper about question-answering (Q & A) systems based on Retrieval-Augmented Generation (RAG) performed a comparison between generic and domain-tuned models (Nguyen et al., 2024, p.13). A fine-tuned embedding model combined with a domain-tuned LLM showed a much better accuracy, especially based on the improvements of the embeddings.

Scientific Reasoning – Applying Reinforcement Fine Tuning (RFT) to our models of the reasoning foundations (OpenRFT), we found that it shows impressive performance improvement with very few domain-specific examples, only 100 on SciKnowEval tasks.

Azure AI Foundry (Customer Service) – Decagon AI fine-tuning of GPT-4o-mini to support tasks increased the accuracy and reduced the latency (Frame, 2025). CEO Ashwin Sreenvias reveals that the process has sped up the delivery schedule in an accelerated manner.

Embedding Feedback Loops into Agent Behavior

To adopt agile fine-tuning in the field, AI agents should be equipped with real-time feedback loops. Prompt-feedback analytics measures such as task engagement, confusion rates, and prompt success and constantly updates the model. Sentiment-based versions operate with the help of AI symptom analysis to keep an eye on the satisfaction of the users, and thus, sprint teams can tweak it in accordance with the change in emotional tones during the implementation. The fact that human-in-the-loop (HILT) processes encompass expert feedback further accelerates the accuracy, but there is a problem of loss of trust despite the technical advancement, as depicted in a study conducted on interactive image detection (arxiv.org).

Integrating RLHF and RFT into Agile Pipelines

Current agile AI pipelines integrate Reinforcement Fine-Tuning (RFT) and Reinforcement Learning with Human Feedback (RLHF) techniques to maintain model training in line with those of the users. In every sprint, feedback from users is captured and then utilized to train reward models, which are then optimized through methods such as Proximal Policy Optimization (PPO) and process-aware RFT models. This is demonstrated with applicability to scientific tasks. OpenRFT can enhance the reasoning performance with only 100 samples, a phenomenon made possible by using RFT (Zhang et al., 2024). It forms a fast-iterating loop: collect >reward > optimize> deploy > repeat.

Metric That Counts: Metrics that Matter More than Accuracy

Accuracy alone is not enough to determine the success of AI; instead, there must be holistic and user metrics. Along with such classic metrics as precision or latency, being sensitive to such implicit signals as explicit user ratings (surveys, ratings), user session engagement (length, click-throughs), and in-the-moment sentiment unveils an underlying engagement and satisfaction. Correlated dashboards of such dimensions could detect unseen problems such as concept drift or UX friction, which would initiate automatic retraining or UI modifications (Wuisan et al., 2023). This tier-based metrics system is guaranteed to keep AI strong in its technical capabilities and aligned with user value.

Case Study Snapshot: Chase Finance Q and A Assistant

Chase Finance Automation developed a production RAG-based Q&A assistant in Chase Bedrock to automate Accounts Payable/ Receivable. Iterative optimizations made on chunking, prompts, and embeddings increased accuracy to 86 percent, and reduced response time to minutes, compared to days.

Challenges and Recommendations

Agile fine-tuning encounters the following challenges: data drift, overfitting, weakening trust of users, and complexity. Drift- alterations in the data or changes in users' behaviors require automation in the detection of the drifts and their re-training (Kaya & Selcuk, 2025, p.4). Overfitting may be reduced through incorporating lightweight techniques such as LoRA or RFT, which do not need so many samples. The frequency of the feedback must be restricted to ensure trust using explainable AI approaches. The complexity of infrastructure gives way to scalable MLOps pipelines in environments such as Azure or GCP, and the ML and product companies are synchronized through frequent sprint reviews (Aguilar et al., 2021, p.10). The integration of RAG with fine-tuning provides privacy to data and enhances the iteration rate.

References

Aguilar, S., Vidal, R., & Gomez, C. (2021). Evaluation of receiver-feedback techniques for fragmentation over LPWANs. IEEE Internet of Things Journal, 9(9), 6866-6878. https://upcommons.upc.edu/bitstream/handle/2117/360653/FINAL_VERSION-copyright.pdf?sequence=1

Alicia Frame. (2025, April 28). Advancing Fine-Tuning in Azure AI Foundry: April 2025 Updates. Retrieved July 2, 2025, from TECHCOMMUNITY.MICROSOFT.COM website: https://techcommunity.microsoft.com/blog/azure-ai-services-blog/advancing-fine-tuning-in-azure-ai-foundry-april-2025-updates/4408745

Kaya, Y., & Selcuk, R. S. (2025). Assessing a fine-tuned scrum ai agent: Accuracy, utility, and expert validation. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 10(4), 4. https://dialnet.unirioja.es/descarga/articulo/10129330.pdf

LinkedIn. (2024). Sign in - Google Accounts. Retrieved July 2, 2024, from Linkedin.com website: https://www.linkedin.com/pulse/from-waterfall-agile-managing-transition-maximum-benefit-fteyc/

Nguyen, Z., Annunziata, A., Luong, V., Dinh, S., Le, Q., Ha, A. H., ... & Nguyen, C. (2024). Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study. arXiv preprint arXiv:2404.11792. https://arxiv.org/pdf/2404.11792

Pyke, C. (2025, March 10). How to Iterate on AI Product Feedback Quickly – Agile Development for AI Products. Retrieved July 2, 2025, from Kingy AI website: https://kingy.ai/blog/how-to-iterate-on-ai-product-feedback-quickly-agile-development-for-ai-products

Tupsakhare, P. (2022). Enhancing Agile Methodologies with AI: Driving Efficiency and Innovation. European Journal of Advances in Engineering and Technology, 9(10), 66-71. https://www.researchgate.net/profile/Preeti-Tupsakhare/publication/385078222_Enhancing_Agile_Methodologies_with_AI_Driving_Efficiency_and_Innovation/links/67143a3624a01038d0f853a6/Enhancing-Agile-Methodologies-with-AI-Driving-Efficiency-and-Innovation.pdf

Wuisan, D. S. S., Sunardjo, R. A., Aini, Q., Yusuf, N. A., & Rahardja, U. (2023). Integrating artificial intelligence in human resource management: A smartpls approach for entrepreneurial success. Aptisi Transactions on Technopreneurship (ATT), 5(3), 334-345. https://att.aptisi.or.id/index.php/att/article/download/355/241

ZenML (2024) Amazon Finance: Scaling rag accuracy from 49% to 86% in finance Q&A assistant - zenml llmops database, ZenML. https://www.zenml.io/llmops-database/scaling-rag-accuracy-from-49-to-86-in-finance-q-a-assistant

Zhang, Y., Yang, Y., Shu, J., Wang, Y., Xiao, J., & Sang, J. (2024). OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning. arXiv preprint arXiv:2412.16849. https://arxiv.org/pdf/2412.16849