AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
Quick Answer
This paper shows that AINTMA, an autonomous test management architecture utilizing six specialized AI agents, achieves 88.4% test prioritization accuracy and reduces defect escape rates from 8.3% to 2.1%.
Quick Take
The system demonstrates a 340% ROI within nine months, showcasing the potential of agentic AI in enhancing software quality management in cloud environments.
Key Points
- AINTMA employs six AI agents for autonomous quality intelligence in cloud-native environments.
- Achieved 88.4% test prioritization accuracy compared to 51.2% random selection.
- Test cycle time reduced by 43%, enhancing efficiency in software testing.
- Defect escape rate decreased from 8.3% to 2.1%, improving software reliability.
- Scalable architecture supports over 50,000 test cases with sub-400ms response time.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Modern software quality assurance demands intelligent, autonomous systems capable of adaptive decision-making across distributed cloud environments. This paper presents AINTMA (Agentic Intelligent Test Management Architecture), a multi-agent agentic AI system that transforms traditional test management into an autonomous quality intelligence ecosystem. AINTMA deploys six specialized AI agents (Test Discovery, Risk Assessment, Reinforcement Learning Prioritization, Execution Orchestration, Generative Quality Intelligence, and Cloud Security Monitor) coordinated through a secure multi-agent communication framework over a cloud-native microservices infrastructure. The Generative Quality Intelligence agent employs large language models to produce plain language quality narratives, defect risk summaries, and data-augmented test recommendations. The RL Prioritization agent models test selection as a Markov Decision Process, learning contextual policies from large-scale historical test execution data (47 features, rolling 36-month window). Secure cloud communication is enforced through a zero-trust API gateway with OAuth2/JWT authentication, encrypted inter-agent messaging, and multi-tenant isolation. Evaluation across 12 heterogeneous software projects over 18 months demonstrates: 88.4% test prioritization accuracy (APFD, vs. 51.2% random, 82.1% best commercial baseline); 43% test cycle time reduction; defect escape rate reduced from 8.3% to 2.1%; 340% ROI at 9-month payback. The agentic architecture scales to 50,000+ test cases with sub-400ms response time, and the generative intelligence module achieves 4.3/5.0 developer usefulness rating. AINTMA demonstrates that agentic AI, combining autonomous multi-agent coordination, generative intelligence and secure smart connectivity, can fundamentally advance software quality management in cloud-scale enterprise environments.
| Comments: | 11 pages, 2 figures, 4 tables, Submitted to AICCONS (AIP Conference Proceedings format) |
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.11; I.2.6; K.6.5 |
| Cite as: | arXiv:2607.20452 [cs.AI] |
| (or arXiv:2607.20452v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.20452 arXiv-issued DOI via DataCite |
Submission history
From: Vinil Pasupuleti [view email]
[v1]
Thu, 14 May 2026 18:05:21 UTC (19 KB)
— Originally published at arxiv.org
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