Simple Agentic AI workflow for AIOPS (Agentic AIOps)

This paper showcases a simple Agentic AI framework aimed at improving AIOps through the deployment of autonomous, goal-oriented agents. Artificial Intelligence for IT Operations (AIOps) refers to the application of AI and machine learning to make IT operations run more efficiently and automatically. This work demonstrates AI agents for detecting problems, automated baselining deduplication of alerts and remediation. By removing unnecessary alerts, the AI agent for deduplication makes sure that only distinct and actionable alerts move to the next phase. Using anomaly detection algorithms, the operational system identifies unexpected things in operational data streams, such as logs, metrics, and traces. Agents employ ARIMA time-series modeling, and past events to baseline metrics and start resolving automatically in case of anomaly, such as by adding more resources or restarting services. Agents may make incident resolution work better by constantly improving their answers with feedback. This agentic workflow uses smart policy engines, automation frameworks, and observability tools to build a scalable base for proactive and self-healing AIOps settings. Using AI agents to accomplish different tasks in AIOPS makes things operate more smoothly and needs less interference from people.