Article Information

Authors DR.HUSNA SULTANA
Article Type Research Article
Language English
Journal North Asian International Research Journal of Sciences, Engineering & I.T.
ISSN 2454-7514
Volume 12
Issue 1
Pages 26-31
Publication Year 2026
Publication Date January 01, 2026
DOI URL https://doiglobal.org/10.2026/NAIRJCSEIT.005

Abstract

AI-driven modules are reusable software components that perform intelligent functions such as classification, prediction, recommendation, retrieval, generation, perception, planning, and decision support. Rather than constructing every application as a single monolithic model, modern systems increasingly combine specialized modules with databases, business rules, external tools, user interfaces, and human review. This paper examines the concept, architecture, lifecycle, and applications of AI-driven modules. It proposes a layered design consisting of data, model, retrieval, orchestration, policy, explanation, monitoring, and human-oversight modules. The paper reviews design patterns such as model-as-a-service, retrievalaugmented generation, mixture-of-experts, agentic tool use, and event-driven inference. It also discusses integration problems, including data contracts, latency, security, versioning, evaluation, observability, and cascading failure. The paper argues that modular AI can improve reuse, maintainability, scalability, and governance, but only when interfaces and responsibilities are explicit. Areference framework is presented for building AI modules that are reliable, replaceable, auditable, and aligned with organizational objectives.

Keywords

AI-driven modules modular AI software architecture retrieval-augmented generation intelligent agents MLOps responsible AI

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DOI: 10.2026/NAIRJCSEIT.005

References

1. Amershi, S., et al. (2019). Software engineering for machine learning: A case study. Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice, 291-300.
2. Autio, C., et al. (2024). Artificial intelligence risk management framework: Generative artificial intelligence profile (NIST AI 600-1).
3. Breck, E., et al. (2017). The ML test score: A rubric for ML production readiness and technical debt reduction. IEEE Big Data.
4. Huyen, C. (2022). Designing machine learning systems. O’Reilly Media.
5. Lewis, P., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.

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DR.HUSNA SULTANA (2026). AI-DRIVEN MODULES IN MODERN SOFTWARE SYSTEMS: ARCHITECTURE, INTEGRATION, GOVERNANCE, AND APPLICATIONS. North Asian International Research Journal of Sciences, Engineering & I.T.. DOI: https://doiglobal.org/10.2026/NAIRJCSEIT.005

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