Operations | Monitoring | ITSM | DevOps | Cloud

For a small practice, choosing an electronic health record system affects nearly every part of the working day: documenting visits, prescribing medications, reviewing results, scheduling appointments, and submitting claims. The right choice should fit your clinical specialty and the way your team operates.

Building automation systems help facility managers monitor equipment, optimize building performance, and detect potential issues before they become major disruptions. However, detecting an issue is only the first step. Ensuring the right technician receives and responds to a critical alarm is equally important. Johnson Controls Metasys is a building automation system (BAS) that enables facility teams to monitor and control HVAC equipment, lighting, energy usage, and other connected building systems.

The best incident management software in 2026 depends on your team’s response workflow: OnPage for SMB and enterprise IT and digital operations teams prioritizing persistent critical alerts and on-call escalation, PagerDuty for large organizations with complex routing and integration requirements, and incident.io for engineering teams coordinating incidents in Slack.

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.

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.

Effective cloud monitoring and incident response starts with understanding service health, collecting useful diagnostic data, and establishing a clear process for investigating and resolving problems. Whether you use AWS, Azure, Google Cloud, or several providers, the goal is to understand how your systems are performing and take action when reliability is at risk. A dashboard showing healthy virtual machines does not necessarily mean customers can complete a transaction.

IT incidents do not follow a 9-to-5 schedule. A server can go down overnight, a critical application can fail on a weekend, or a high-priority support ticket can arrive when the IT team has already left the office. For IT teams, the challenge goes beyond simply providing after-hours IT support. They need a reliable on-call process that identifies the right responder, delivers critical alerts and ensures timely escalation when necessary.

To manage cloud alerts effectively, define which conditions require immediate action, include useful incident context, and route critical notifications to the engineer currently on call. Add escalation rules for unanswered alerts and test the entire path from detection to response. This process applies whether your team uses AWS, Azure, Google Cloud, or several providers. Cloud monitoring can tell you something is wrong.

The best AI SRE tools to evaluate in 2026 include Sherlocks AI, Resolve AI, Traversal, Cleric, Rootly AI SRE, Komodor Klaudia, Datadog Bits Investigation, and Dynatrace Intelligence. Each addresses a different part of reliability work, from investigating alerts across multiple systems to diagnosing Kubernetes failures or validating production changes. Choosing the right tool starts with a practical question: where does your team lose time during an incident?

As businesses increasingly integrate artificial intelligence into their applications and workflows, understanding what happens behind the scenes of these AI systems has become essential. AI-powered chatbots, large language models (LLMs), and autonomous AI agents can process thousands of requests, interact with external applications, and execute complex tasks. However, when something goes wrong, identifying the source of the problem isn’t always straightforward. Was the AI response incorrect?