# AIOps Published articles for AIOps. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Connect Prometheus Alerts to an Event-Driven AI Agent for Initial Investigation DevFeed: [How to Connect Prometheus Alerts to an Event-Driven AI Agent for Initial Investigation]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-07-23T15:00:31Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [AI Agent](), [Prometheus](), [event driven](), [Kubernetes](), [Slack](), [Deployment]() Tags: [agents](), [ai](), [ai-agents](), [aiops](), [alert-fatigue](), [alert-manager](), [alert-triage](), [architecture](), [auto-remediation](), [automated-incident-response](), [automation](), [devaiops](), [event-driven](), [event-driven-ai-agents-with-prometheus-alerts](), [guide](), [how-to](), [kubernetes](), [kubernetes-ai-agent](), [prometheus](), [prometheus-alert-rules](), [prometheus-alertmanager-webhook](), [root-cause-analysis](), [slack](), [sre](), [sre-automation]() ### AI overview This guide explains how to connect Prometheus and Alertmanager to an event-driven AI agent that investigates alerts before a human responds. It covers the architecture, read-only investigation tools, alert-rule annotations, safety guardrails, and a progression toward guarded remediation. ### Source excerpt Every page interrupts a human, yet most alerts end in the same ten investigation steps. Here is how event driven AI agents catch Prometheus alerts and do that first pass before you even look at your phone. ## IA en sysadmin: ¿oportunidad o amenaza? DevFeed: [IA en sysadmin: ¿oportunidad o amenaza?]() Original publisher: [Read original article]() Author: Alois Published: 2026-01-13T00:00:00Z Content type: opinion Language: es Sources: [tengoping.com]() Topics: [AIOps](), [datacenter](), [Infrastructure as code](), [Terraform](), [Ansible](), [Command-line interface](), [Post Mortem](), [telemetry]() Tags: [aiops](), [ansible](), [datacenter](), [iac](), [logs](), [post-mortem](), [sysadmin](), [terminal](), [terraform]() ### AI overview This Spanish-language article examines how AI can assist systems administrators with log triage, anomaly detection, IaC manifest generation, incident summaries, runbook drafts, and command suggestions. It argues that AI reduces mechanical work but does not replace human diagnosis, review, or operational judgment. ### Source excerpt Reflexión sobre dónde ayuda la IA al sysadmin, qué riesgos reales conlleva en producción y cómo adaptarse sin perder criterio. ## From siloed DataOps, MLOps, and LLMOps to a unified data-intelligence platform DevFeed: [From siloed DataOps, MLOps, and LLMOps to a unified data-intelligence platform]() Original publisher: [Read original article]() Author: Rajit Saha Published: 2025-08-04T18:03:19Z Content type: opinion Language: en Sources: [Udemy Engineering]() Topics: [DataOps](), [MLOps](), [Generative AI](), [Apache Spark](), [Amazon Bedrock](), [Amazon S3](), [Amazon Redshift](), [Amazon SageMaker](), [apache-flink]() Tags: [ai](), [aiops](), [amazon-s3](), [analytics](), [apache-flink](), [apache-spark](), [bedrock](), [dataops](), [large-language-models](), [llmops](), [mlops]() ### AI overview The article describes how DataOps, MLOps, and AI/LLM Ops commonly rely on separate systems and teams for data processing, model deployment, inference, evaluation, orchestration, governance, and monitoring. It then introduces Databricks' Data Intelligence Platform as a unified environment intended to bring these domains together. ### Source excerpt Introduction In modern data-driven businesses, the pace of innovation in analytics and artificial intelligence has outstripped the capacity of many teams. Three distinct disciplines emerged to handle this expansion: Data platform (DataOps) teams built data lakes on cloud storage such as Amazon S3, processed them with Apache Spark and Hive on EMR, ingested streaming data with Spark Structured Streaming or Apache Flink, and loaded tabular copies into MPP warehouses like Redshift for interactive SQL and BI. Cataloguing and governance were offloaded to external tools such as DataHub, and fine-grained access controls required third-party services like Privacera. This architecture worked, but it required separate workflows for batch and streaming, extra systems for lineage and governance, and a mosaic of operational teams. MLOps teams provided an additional layer. Data scientists used notebook environments (for example, Amazon SageMaker) to preprocess data, train, and evaluate models. Deploying models meant writing integration code to move features into a serving layer, to register models in disparate registries and to build custom APIs for inference. Feature stores and model registries were bought from additional vendors. Updates and monitoring were often manual processes. AI/LLM Ops teams are a new addition because generative AI requires specialized components: LLM gateways (e.g., Amazon Bedrock) to proxy access to foundation models; evaluation tooling to compare large language models; orchestration frameworks for agents; vector databases for retrieval augmented generation; and of course another layer of security, access management and cost control. These tools seldom integrate seamlessly with existing data and ML pipelines. This fragmented state makes it difficult to react quickly when product requirements change. Each new capability requires another system, another integration, and another team. Meanwhile, budgets tighten and go-to-market timelines shrink. The questio ## What AI Tools Mean for Deployment, Operations and Security (AI for DevEx, Part 5) DevFeed: [What AI Tools Mean for Deployment, Operations and Security (AI for DevEx, Part 5)]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2023-12-16T05:00:00Z Content type: article Language: en Sources: [Tobias Mende]() Topics: [Deployment](), [Monitoring](), [DevOps](), [Security](), [DevSecOps](), [AIOps](), [SIEM, Security, Observability]() Tags: [ai](), [ai-for-devex-artificial-intelligence-developer-experience-developer-productivity](), [ai-tools](), [aiops](), [datadog](), [deployment](), [deployments](), [devops](), [devsecops](), [errors](), [health-checks](), [http](), [monitoring](), [operations](), [production](), [recovery](), [security](), [software](), [software-engineering](), [tools]() ### AI overview This article examines how AI tools may reduce cognitive load for software and platform teams responsible for deployment, operations, monitoring, and security. It discusses automated log and trace monitoring, anomaly detection, alerting, health checks, HTTP 500 errors, and tools that help identify root causes and speed recovery. ### Source excerpt What AI Tools Mean for Deployment, Operations and Security (AI for DevEx, Part 5) Welcome back to AI for DevEx, Part 5. In the last part, we explored how AI tools can help us to improve our collaboration and communication. Today, we're focusing on an often ignored aspect of software engineering: the importance of deployment, operation, monitoring, and security for teams that manage their software from creation to running it in production. Terms like DevOps, DevSecOps, and DevOps have been created to emphasize the need for teams to own their software completely.