Computer Science > Artificial Intelligence
arXiv:2607.20452 (cs) | Submitted on 14 May 2026
Authors
Vinil Pasupuleti, Shyalendar Reddy Allala, Siva Rama Krishna Varma Bayyavarapu, Shrey Tyagi, Srinivasateja Songa
Abstract
Modern software quality assurance demands intelligent, autonomous systems capable of adaptive decision-making across distributed cloud environments. This paper introduces AINTMA (Agentic Intelligent Test Management Architecture), a multi-agent 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 built on a cloud-native microservices infrastructure.
Generative Quality Intelligence Agent: Leverages large language models (LLMs) to produce plain-language quality narratives, defect risk summaries, and data-augmented test recommendations. As of 2026, LLM-based agents in QA pipelines have become increasingly common, with AINTMA demonstrating a 4.3/5.0 developer usefulness rating.
RL Prioritization Agent: Models test selection as a Markov Decision Process (MDP), learning contextual policies from large-scale historical test execution data (47 features across a rolling 36-month window).
Secure Cloud Communication: Enforced through a zero-trust API gateway with OAuth2/JWT authentication, encrypted inter-agent messaging, and multi-tenant isolation—critical for enterprise adoption given the 2026 landscape of heightened cybersecurity regulations and cloud-native deployments.
Evaluation Results
Over 18 months across 12 heterogeneous software projects, AINTMA achieved:
- 88.4% test prioritization accuracy (APFD), outperforming random selection (51.2%) and the best commercial baseline (82.1%)
- 43% reduction in test cycle time
- Defect escape rate reduced from 8.3% to 2.1%
- 340% ROI with a 9-month payback period
- Scalability to 50,000+ test cases with sub-400ms response time
- 4.3/5.0 developer usefulness rating for the generative intelligence module
Conclusion
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. The architecture is particularly relevant for organizations adopting AI-driven DevOps and continuous testing in 2026.
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
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