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The latest News and Information on Incident Management, On-Call, Incident Response and related technologies.

Running AI at Enterprise Scale w/ Anthropic, Descope, Port, Rootly and Twingate

The debate about whether AI can write production code is over. Companies are handing work to fleets of agents, and for many, they write most of the code that ships to production. The next challenge is everything that happens once an entire engineering organization runs this way, at full speed. Teams that generate code 10x faster still review it at human speed, and that mismatch is now the constraint. Code ownership is also becoming an issue, as developers learn to trust agentic processes a little too much. When an agent breaks production, who is responsible?

How AI Improves Service Desk Automation and Client Experience

Artificial intelligence is reshaping the IT service desk, moving it from a reactive cost center to a proactive, value-driven business partner. By automating repetitive tasks and providing deep analytical insights, AI helps IT teams resolve issues faster and deliver a superior client experience. This shift allows support staff to focus on more complex challenges, improving both efficiency and employee morale. The result is a more agile and responsive IT support system that directly contributes to organizational success.

From Detection to Resolution: Why ServiceNow + xMatters Is the Fastest Path to Incident Resolution

AI is changing incident management, but not in the way most people think. For years, operations teams focused on getting better at detecting problems. Monitoring improved. Observability improved. AI is now helping teams correlate signals, reduce noise, and identify issues faster than ever before. That’s all valuable, but many organizations are discovering that finding the problem is no longer the hardest part. The harder part is everything that happens next. Who owns the issue?

How to Build Escalations That Actually Work

Most IT teams already know when something breaks. The real problem is making sure the right person responds fast enough. A server goes down. A customer-facing application crashes. A security alert triggers after hours. The monitoring system sends the notification. But nobody responds. The alert gets buried in Slack. The on-call engineer misses the push notification. The wrong person is scheduled. Everyone assumes somebody else is handling it. That is how small incidents become expensive outages.

ER-to-Physician Communication Workflow: Healthcare Critical Alerting Case Study

When a nurse calls for help, every second counts. ER nurses juggle a lot: admission decisions, discharge approvals, orders, physician consults. When they need support fast, they can't afford to chase down the right person manually. Here's how one physician-led medical group solved it using OnPage: Nurses leave a voicemail on a single intake line It's automatically routed into OnPage as an alert to the on-call triage coordinator.

PagerDuty User Group Toronto: Incident Enrichment, Automating Maintenance & New Event Capabilities

Recorded during the PagerDuty User Group Toronto, May 2026 - part of Toronto Tech Week. About PagerDuty User Groups: Connect with PagerDuty users, share your experiences, and learn new ways to maximize the power of digital operations. It's a space where technical leaders and practitioners come together to collaborate, solve challenges, and get inspired by each other's successes.

Root Cause Analysis: How Engineering Teams Fix Production Issues Faster?

When a production incident strikes, a sudden latency spike, a cascading API failure, a service returning 500s at scale, every minute of downtime has a cost. Root cause analysis (RCA) is the process that turns that chaos into a clear answer: what actually broke, and why. Not the symptom that triggered the alert. The underlying cause.

Customers over control: how we measure On-call reliability

Our On-call product has a lot of great features: configuring escalation paths, viewing rotas and schedules, requesting cover, etc. However, when framing its reliability, we reduce it down to two critical pieces of functionality: It’s not that we’re happy if only these parts are working, but they are the most important parts. In this post, I'll go into more detail on how we think about their reliability.