ARCHIVES
Year 2026 · Volume 5 · Issue 3
Original Article
Agentic AI Incident Response Platform for Cloud Applications: Telemetry-Grounded Root-Cause Investigation with Human Approval
Veerendeswari J1
Karunagaran V2
Sujeethan S3
Sivabaalan S4
Dhanush R5
1 Assistant Professor& HoD, Department of Information Technology, Rajiv Gandhi College of Engineering and Technology, Puducherry, India 2 3 4 5 UG Scholar, Department of Information Technology, Rajiv Gandhi College of Engineering and Technology, Puducherry, India.
Published Online: September-December 2026
Pages: 330-336
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503039
References
[1]. Ahmed T, Ghosh S, Bansal C, Zimmermann T, Zhang X, Rajmohan S. Recommending root-cause and mitigation steps for cloud incidents using large language models. In: Proc IEEE/ACM 45th Int Conf Softw Eng (ICSE); 2023. p. 1737–49.
[2]. Chen J, He X, Lin Q, Zhang H, Hao D, Gao F, et al. How incidental are the incidents? Characterizing and prioritizing incidents for large-scale online service systems. In: Proc 35th IEEE/ACM Int Conf Autom Softw Eng (ASE); 2020. p. 373–84.
[3]. Chen J, He X, Lin Q, Xu Y, Zhang H, Hao D, et al. Continuous incident triage for large-scale online service systems. In: Proc 34th IEEE/ACM Int Conf Autom Softw Eng (ASE); 2019. p. 364–75.
[4]. Jiang J, Lu W, Chen J, Lin Q, Zhao P, Kang Y, et al. How to mitigate the incident? An effective troubleshooting guide recommendation technique for online service systems. In: Proc 28th ACM Joint Eur Softw Eng Conf Symp Found Softw Eng (ESEC/FSE); 2020. p. 1410–20.
[5]. Bansal C, Renganathan S, Asudani A, Midy O, Janakiraman M. DeCaF: Diagnosing and triaging performance issues in large-scale cloud services. In: Proc IEEE/ACM 42nd Int Conf Softw Eng: Softw Eng Pract (ICSE-SEIP); 2020.
[6]. Luo C, Lou JG, Lin Q, Fu Q, Ding R, Zhang D, et al. Correlating events with time series for incident diagnosis. In: Proc 20th ACM SIGKDD Int Conf Knowl Discov Data Min (KDD); 2014. p. 1583–92.
[7]. Saha A, Hoi SCH. Mining root cause knowledge from cloud service incident investigations for AIOps. In: Proc IEEE/ACM 44th Int Conf Softw Eng: Softw Eng Pract (ICSE-SEIP); 2022.
[8]. Ghosh S, Shetty M, Bansal C, Nath S. How to fight production incidents? An empirical study on a large-scale cloud service. In: Proc 13th ACM Symp Cloud Comput (SoCC); 2022. p. 126–41.
[9]. Chen Z, Kang Y, Li L, Zhang X, Zhang H, Xu H, et al. Towards intelligent incident management: why we need it and how we make it. In: Proc 28th ACM Joint Eur Softw Eng Conf Symp Found Softw Eng (ESEC/FSE); 2020. p. 1487–97.
[10]. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: Adv Neural Inf Process Syst (NeurIPS); 2017. p. 5998–6008.
[11]. Yao S, Zhao J, Yu D, Du N, Shafran I, Narasimhan K, et al. ReAct: Synergizing reasoning and acting in language models. In: Proc Int Conf Learn Represent (ICLR); 2023.
[12]. Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv Neural Inf Process Syst (NeurIPS). 2020;33:9459–74.
[13]. Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In: Proc Conf Empir Methods Nat Lang Process (EMNLP-IJCNLP); 2019. p. 3982–92.
[14]. Malkov YA, Yashunin DA. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Trans Pattern Anal Mach Intell. 2020;42(4):824–36.
[15]. Chen Y, Xie H, Ma M, Kang Y, Gao X, Shi L, et al. Automatic root cause analysis via large language models for cloud incidents. In: Proc 19th Eur Conf Comput Syst (EuroSys); 2024.
[16]. Wang Z, Liu Z, Zhang Y, Zhong A, Wang J, Yin F, et al. RCAgent: Cloud root cause analysis by autonomous agents with tool-augmented large language models. In: Proc 33rd ACM Int Conf Inf Knowl Manag (CIKM); 2024.
[17]. Roy D, Zhang X, Bhave R, Bansal C, Las-Casas P, Fonseca R, et al. Exploring LLM-based agents for root cause analysis. In: Companion Proc 32nd ACM Int Conf Found Softw Eng (FSE); 2024.
[2]. Chen J, He X, Lin Q, Zhang H, Hao D, Gao F, et al. How incidental are the incidents? Characterizing and prioritizing incidents for large-scale online service systems. In: Proc 35th IEEE/ACM Int Conf Autom Softw Eng (ASE); 2020. p. 373–84.
[3]. Chen J, He X, Lin Q, Xu Y, Zhang H, Hao D, et al. Continuous incident triage for large-scale online service systems. In: Proc 34th IEEE/ACM Int Conf Autom Softw Eng (ASE); 2019. p. 364–75.
[4]. Jiang J, Lu W, Chen J, Lin Q, Zhao P, Kang Y, et al. How to mitigate the incident? An effective troubleshooting guide recommendation technique for online service systems. In: Proc 28th ACM Joint Eur Softw Eng Conf Symp Found Softw Eng (ESEC/FSE); 2020. p. 1410–20.
[5]. Bansal C, Renganathan S, Asudani A, Midy O, Janakiraman M. DeCaF: Diagnosing and triaging performance issues in large-scale cloud services. In: Proc IEEE/ACM 42nd Int Conf Softw Eng: Softw Eng Pract (ICSE-SEIP); 2020.
[6]. Luo C, Lou JG, Lin Q, Fu Q, Ding R, Zhang D, et al. Correlating events with time series for incident diagnosis. In: Proc 20th ACM SIGKDD Int Conf Knowl Discov Data Min (KDD); 2014. p. 1583–92.
[7]. Saha A, Hoi SCH. Mining root cause knowledge from cloud service incident investigations for AIOps. In: Proc IEEE/ACM 44th Int Conf Softw Eng: Softw Eng Pract (ICSE-SEIP); 2022.
[8]. Ghosh S, Shetty M, Bansal C, Nath S. How to fight production incidents? An empirical study on a large-scale cloud service. In: Proc 13th ACM Symp Cloud Comput (SoCC); 2022. p. 126–41.
[9]. Chen Z, Kang Y, Li L, Zhang X, Zhang H, Xu H, et al. Towards intelligent incident management: why we need it and how we make it. In: Proc 28th ACM Joint Eur Softw Eng Conf Symp Found Softw Eng (ESEC/FSE); 2020. p. 1487–97.
[10]. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: Adv Neural Inf Process Syst (NeurIPS); 2017. p. 5998–6008.
[11]. Yao S, Zhao J, Yu D, Du N, Shafran I, Narasimhan K, et al. ReAct: Synergizing reasoning and acting in language models. In: Proc Int Conf Learn Represent (ICLR); 2023.
[12]. Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv Neural Inf Process Syst (NeurIPS). 2020;33:9459–74.
[13]. Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In: Proc Conf Empir Methods Nat Lang Process (EMNLP-IJCNLP); 2019. p. 3982–92.
[14]. Malkov YA, Yashunin DA. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Trans Pattern Anal Mach Intell. 2020;42(4):824–36.
[15]. Chen Y, Xie H, Ma M, Kang Y, Gao X, Shi L, et al. Automatic root cause analysis via large language models for cloud incidents. In: Proc 19th Eur Conf Comput Syst (EuroSys); 2024.
[16]. Wang Z, Liu Z, Zhang Y, Zhong A, Wang J, Yin F, et al. RCAgent: Cloud root cause analysis by autonomous agents with tool-augmented large language models. In: Proc 33rd ACM Int Conf Inf Knowl Manag (CIKM); 2024.
[17]. Roy D, Zhang X, Bhave R, Bansal C, Las-Casas P, Fonseca R, et al. Exploring LLM-based agents for root cause analysis. In: Companion Proc 32nd ACM Int Conf Found Softw Eng (FSE); 2024.
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