Cloud-native analytics pipelines are vulnerable to schema drift, implicit data-contract violations, and cascading service-level objective (SLO) failures that conventional monitoring or manual DataOps may detect only after downstream impact. This paper presents AegisFlow, a policy-aware agentic orchestration framework f...
Preethi Kamalakannan, Shania Rasheed Nalagath, Bhakti Hinduja et al.· International Workshop on Ar...· 0 citations
Deployed large language model (LLM) agents are now being used to interface with external tools, fetch information, run code, interact with user data and help with decision making at the workflow level. Therefore, their safety issues are not only related to the underlying model, but also to tool permissions, prompt desi...
Aakash Abhay Yadav, Shashank Shelat, B. Hinduja et al.· International Conference on...· 0 citations
The more typical feature of agentic AI systems is dynamic, multistep workflows where autonomous components plan, reason, and communicate with external tools and data sources in a series of iterations. Such flexibility increases capability but also brings nondeterminism which is inherent and where the same inputs can re...
Ankur Gupta, Karan Gupta, Divyakumar Deepak Savla et al.· International Conference on...· 0 citations
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