The Scrum framework is one of the most popular Agile software development frameworks, due to its versatility, iterative development process, and focus on delivering value in small increments. However, sprint overruns continue to be a significant operational challenge and are usually caused by changes in requirements, misjudging the effort, blocker events, or productivity swings. Scrum monitoring methods currently used are mostly related to progress tracking and predictive analytics, offering little assistance in the proactive aspect when the sprint runs. This study presents a lightweight Rule-Based Agentic Mitigation Framework that actively observes deviations in sprint execution and takes pre-defined corrective actions if execution risk is above defined thresholds. The proposed framework is not dependent on large historical databases or complex computational infrastructure, as is the case for machine learning-based approaches, thus enabling the use of the framework in various Agile environments. To assess the effectiveness of the proposed framework, an experimental simulation was used. All 200 paired sprint simulations were performed under identical conditions of scope changes, blocker occurrences, and velocity variability. The following metrics were used to evaluate the performance: Sprint Overrun Rate, Sprint Success Rate, Average Risk Detection Day, and Execution Stability Index. The results of the experiments showed that the proposed framework improved the Sprint Overrun Rate from 0.830 to 0.625, which is an improvement of 24.7%. The Sprint Success Rate went from 17.0% to 37.5%, and Average Risk Detection Day went from Day 5.34 to Day 3.19, allowing earlier corrective action. Additionally, the Index of Execution Stability rose from 0.78 to 0.92, which means there was greater consistency in sprint progress. In the ablation study, it was confirmed that the totality of the velocity correction, scope adjustment, and ambiguity reduction resulted in improvement of the performance. The results indicate that agentic mitigation by rules is an effective and practical solution to increase the reliability of sprints, decrease overruns, and improve Agile project governance.
Vestues K, Hanssen GK, Mikalsen M, Buan TA, Conboy K. Agile Data Management in NAV: A Case Study. Lecture Notes in Business Information Processing. Springer International Publishing; 2022. p. 220–35.
2.
Upender B. Staying agile in government software projects. Agile Development Conference (ADC’05). IEEE Comput. Soc; p. 153–9.
3.
Chołda P, Jaglarz P. Optimization/simulation-based risk mitigation in resilient green communication networks. Journal of Network and Computer Applications. 2016;59:134–57.
4.
Segundo C, Oliveira M, Gonçalves E. An Agent-Based Simulation for Task Allocation in Software Development Teams based on the Truck Factor Metric. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal. 2025;14:e32600.
5.
Matthies C. Playing with your project data in scrum retrospectives. Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings. ACM; 2020. p. 113–5.
6.
Fawzy A, Tahir A, Galster M, Liang P. Exploring data management challenges and solutions in agile software development: a literature review and practitioner survey. Empirical Software Engineering. 2025;30(3).
7.
Rieger CG, Villez K. Resilient control system execution agent (ReCoSEA). 2012 5th International Symposium on Resilient Control Systems. IEEE; 2012. p. 143–8.
8.
Popli R, Chauhan N. A sprint-point based estimation technique in Scrum. 2013 International Conference on Information Systems and Computer Networks. IEEE; 2013. p. 98–103.
9.
Lalith M. Agent-Based Simulation for Earthquake Disaster Mitigation. ASEAN School on High-Performance Computing and Artificial Intelligence. ACM; 2025. p. 1.
10.
Radhakrishnan U, Koumaditis K. Teaching Scrum with a Virtual Sprint Simulation: Initial Design and Considerations. 26th ACM Symposium on Virtual Reality Software and Technology. ACM; 2020. p. 1–2.
11.
Qureshi JN, Farooq MS, Khelifi A, Atal Z. Harnessing the Potential of Blockchain in ChainAgilePlus Framework for the Improvement of Distributed Scrum of Scrums Agile Software Development. IEEE Access. 2024;12:105724–43.
12.
Fagarasan C, Cristea C, Cristea M, Popa O, Pisla A. Integrating Sustainability Metrics into Project and Portfolio Performance Assessment in Agile Software Development: A Data-Driven Scoring Model. Sustainability. 2023;15(17):13139.
13.
Dautov R, Husom EJ, Gonidis F. Towards MLOps in Mobile Development with a Plug-in Architecture for Data Analytics. 2022 6th International Conference on Computer, Software and Modeling (ICCSM). IEEE; 2022. p. 22–7.
14.
Canedo ED, Calazans ATS, Bandeira IN, Costa PHT, Masson ETS. Guidelines adopted by agile teams in privacy requirements elicitation after the Brazilian general data protection law (LGPD) implementation. Requirements Engineering. 2022;27(4):545–67.
15.
Barbala A, Sporsem T, Stray V. Data-Driven Development in Public Sector: How Agile Product Teams Maneuver Data Privacy Regulations. Lecture Notes in Business Information Processing. Springer Nature Switzerland; 2023. p. 165–80.
16.
Ampatzoglou A, Bibi S, Avgeriou P, Chatzigeorgiou A. Guidelines for Managing Threats to Validity of Secondary Studies in Software Engineering. Contemporary Empirical Methods in Software Engineering. Springer International Publishing; 2020. p. 415–41.
17.
Cholda P, Guzik P, Rusek K. Risk mitigation in resilient networks. 2014 6th International Workshop on Reliable Networks Design and Modeling (RNDM). IEEE; 2014. p. 23–30.
ForouzeshNejad AA, Arabikhan F, Gegov A, Jafari R, Ichtev A. Data-Driven Predictive Modelling of Agile Projects Using Explainable Artificial Intelligence. Electronics. 2025;14(13):2609.
20.
Azonuche TI, Enyejo JO. Exploring AI-Powered Sprint Planning Optimization Using Machine Learning for Dynamic Backlog Prioritization and Risk Mitigation. International Journal of Scientific Research and Modern Technology. 2024;40–57.
21.
Leeuw RT, Joseph N. Reciprocal Influence between Digital Emotional Intelligence and Agile Mindset in an Agile Environment. Administrative Sciences. 2023;13(11):228.
22.
Usman M, Mendes E, Weidt F, Britto R. Effort estimation in agile software development. Proceedings of the 10th International Conference on Predictive Models in Software Engineering. ACM; 2014. p. 82–91.
23.
Verwijs C, Russo D. Do Agile scaling approaches make a difference? an empirical comparison of team effectiveness across popular scaling approaches. Empirical Software Engineering. 2024;29(4).
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