Operations | Monitoring | ITSM | DevOps | Cloud

Running an LLM on Kubernetes is rarely just a Deployment with vLLM in it anymore. A whole layer of frameworks and operators has grown up for it: KServe, NVIDIA Dynamo, Ray Serve, Kthena, llm-d. Each one adds its own custom resource that describes a model deployment, with routing, autoscaling and the split of a model across pods built in, and each one turns that resource into ordinary Kubernetes objects in its own way.

Resque 3.2.0 shipped on October 16 with a single change, listed under Security: resque-web now rejects state-changing requests that come from another site. Until this release, resque-web had no cross-site request forgery (CSRF) protection at all. A page on any other site could submit a form to your dashboard and clear your failed jobs or delete a queue, as long as the browser it ran in could reach resque-web and was logged in to it. The fix is small and most teams can upgrade without touching anything.

“AI workflow tool” now describes products with very different build models. One may generate an executable workflow from a plain-language request. Another may add a chat panel or autocomplete to the same drag-and-drop experience it has offered for years. That difference matters to buyers because teams choose workflow tools to reduce automation backlogs, eliminate repetitive work, and extend orchestration across ITSM, cloud, network, identity, and infrastructure systems.

A decision model is a specialized AI model that evaluates inputs against predefined outcomes, returning a structured choice, classification, or score. Unlike large language models (LLMs), which handle a broad range of generative and reasoning tasks, decision models are optimized for tasks like routing requests, selecting agent actions, and evaluating AI outputs.

Cut alert fatigue and resolve incidents faster. Learn how to design tests, route alerts to the right owning team, and connect internal and Internet signals.

Correlate internal infrastructure health with Internet-path data to localize WAN slowdowns fast across MPLS, SD-WAN, and direct Internet access at the branch.

Jira round robin assignment hands each new issue to the next person on a list, so incoming work spreads evenly instead of piling up on whoever happens to be watching the queue. Jira Cloud can do this natively with Jira Automation, and for most teams that takes ten minutes to set up. The hard part is everything around it: people on leave, triage that happens in Slack instead of Jira, and the gap between "evenly assigned" and "actually picked up".

Quick Summary: NetBrain is our lead pick for enterprise teams evaluating agentic network operations because it combines live network context, AI-assisted investigation, and governed workflows across multi-vendor infrastructure. Other platforms worth considering include Cisco AgenticOps, HPE Juniper Mist with Marvis AI, IBM Network Intelligence, Arista AVA, and Nokia Network Services Platform. Disclosure: This guide is published by OnPage, a provider of incident alerting and on-call management software.

Artificial intelligence (AI) is changing how IT teams manage service requests, troubleshoot technical issues, and respond to incidents. As IT environments become increasingly complex, traditional service management processes can struggle to keep up with growing ticket volumes, recurring incidents, and rising expectations for faster resolutions.