Toward Agentic AI Frameworks for Technical Triage and Automated Product Support

Abstract

Agentic AI systems have achieved wide adoption in software development, assisting engineers with code generation, diagnostics, and efficiency. However, the evolution of AI-powered agents for support engineers and IT operations remains nascent. This paper surveys the landscape of agentic AI in both software development and operational support, examines current capabilities such as observability, root cause analysis, and explores the potential for automated remediation, knowledge-based case resolution, and SME-guided learning. We propose a taxonomy of support-centric AI agent tasks and discuss research and engineering challenges for autonomous, end-to-end product support by AI.

Introduction

Recent advances in agentic AI—intelligent agents capable of autonomous decision-making—have significantly impacted software development and IT operations. In software development, AI-powered coding assistants accelerate planning, generate and review code, optimize performance, manage testing, and automate deployment, reducing cognitive load and improving efficiency. These agents also maintain dynamic documentation and optimize system performance proactively.

In IT operations and product support, agentic AI enhances observability, predictive alerting, and autonomous remediation. AI agents detect anomalies, forecast potential failures, and execute corrective actions, enabling self-healing systems and reducing downtime. They assist IT administrators with root cause analysis (RCA), automated decision-making, and escalation workflows, while supporting engineers with automated diagnostics, knowledge retrieval via RAG models, SME-guided learning, and advanced incident insights. Explainable AI (XAI) and human-in-the-loop controls ensure transparency, trust, and compliance.

The taxonomy of support-focused AI tasks includes decision automation, escalation management, incident triage, knowledge retrieval, and progressive learning. Adoption of agentic AI requires a structured 5-step framework: identifying high-impact use cases, running value-first pilots, establishing governance, scaling with continuous learning, and addressing pitfalls in data quality, security, and organizational culture. Properly deployed, agentic AI enhances operational efficiency, accelerates problem resolution, and empowers human engineers to focus on strategic innovation.

Conclusion

Agentic AI systems are redefining the future of software development and operational support by automating routine activities, expediting diagnosis, and augmenting decision-making. The road map outlined in this paper underscores the trans formative potential—and ongoing challenges—of deploying AI agents in support engineering. By evolving from coding assistants to autonomous operational partners, agentic AI will ultimately drive efficiency, reliability, and resilience across the software product life cycle.

References

[1] N. Kshetri, \"Revolutionizing Higher Education: The Impact of Artificial Intelligence Agents and Agentic Artificial Intelligence on Teaching and Operations,\" in IT Professional, vol. 27, no. 2, pp. 12-16. [2] V. Tupe and S. Thube, \"Demonstrating Multi-Agent Collaboration via Agent-to-Agent and Model Context Protocols: An IT Incident Response Case Study,\" 2025 IEEE International Conference on Service-Oriented System Engineering (SOSE), Tucson, AZ, USA, 2025, pp. 1-5 [3] Tanveer Aamina, Mohammed Zaid, Syeda Huda.\"Evaluating Multi-Agent AI Systems for Automated Bug Detection and Code Refactoring\", Volume 13, Issue X, International Journal for Research in Applied Science and Engineering Technology (IJRASET) Page No: 12-21, ISSN : 2321-9653, www.ijraset.com. [4] Tanveer Aamina, Mohammed Zaid, Syeda Huda.\"Evaluating Multi-Agent AI Systems for Automated Bug Detection and Code Refactoring\", Volume 13, Issue X, International Journal for Research in Applied Science and Engineering Technology (IJRASET) Page No: 12-21, ISSN : 2321-9653, www.ijraset.com [5] Tanveer Aamina, Mohammed Zaid, Syeda Huda.\"Evaluating Multi-Agent AI Systems for Automated Bug Detection and Code Refactoring\", Volume 13, Issue X, International Journal for Research in Applied Science and Engineering Technology (IJRASET) Page No: 12-21, ISSN : 2321-9653, www.ijraset.com

Copyright

Copyright © 2025 Sri Krishna Ravulapalli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.