Operations | Monitoring | ITSM | DevOps | Cloud

AI for Everything After Code: Ship Fast, Stay Safe

Recorded at @DevOpsLive Most teams have “done DevOps” and “built a platform,” but still wrestle with the same core problems: platforms that developers dodge, AI that accelerates coding while quietly degrading delivery performance, security and compliance that can’t keep up, cloud bills that keep climbing, and incident response that hasn’t caught up with cloud‑native complexity.

PagerDuty Invests in the AI-First Operations and Resilience of Healthcare and Crisis Response Organizations

At PagerDuty, we believe operational excellence and social impact are inseparable. As AI rapidly transforms how nonprofits operate, our AI and agentic technology empower mission-driven teams to automate complexity and focus their limited resources on what matters most: delivering reliable services that create meaningful impact at scale.

Debugging multi-agent AI: When the failure is in the space between agents

I've been building a multi-agent research system. The idea is simple: give it a controversial technical topic like "Should we rewrite our Python backend in Rust?", and three agents work on it. An Advocate argues for it, a Skeptic argues against, and a Synthesizer reads both briefs blind and produces a balanced analysis. Each agent has its own model, its own tools, its own system prompt. It worked great in testing. Then I noticed the Synthesizer kept producing analyses that leaned heavily toward one side.

A Prototype's Worth 1,000 Minutes: How Claude Prototypes Accelerate The Product Planning Process

The relationship between product managers (PMs) and engineers is due for an upgrade. The division between these personas is responsible for a healthy, if laborious, collaboration when envisioning and building new products. A PM generates the vision; engineers translate it into an architectural approach, raising the technical questions that sharpen it along the way. This back-and-forth eventually produces tight alignment, a solid PRD, and functional code.

You're Running Agents. Your Tooling Is Still Catching Up.

Introducing GitKraken Desktop 12.0. At some point in the last year, the question shifted. It stopped being “should I use AI coding agents?” and became “how do I run more than one at a time without losing my mind?” If you’ve been there, you know what the management layer looks like. A terminal per agent. A worktree created by hand before each session.

What Parents Should Know About AI Essay Grader Tools

Artificial intelligence is showing up in more classrooms than ever before, and parents are right to have questions. One area that has grown quickly is AI-powered writing assessment. Schools and teachers are increasingly turning to automated tools to help manage the workload of grading student essays, and while this might sound like a behind-the-scenes administrative change, it directly affects how your child receives feedback on their writing. Understanding what these tools do, how they work, and what they cannot do will help you stay informed and involved in your child's education.
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How to Set Up Raygun's Remote MCP Server in Cursor and Codex

After introducing Raygun's original MCP server and our new remote-first version, the most common question we hear is: "How do I actually set this up and start using it?" This guide covers exactly that, two short videos walking through setup and a real error being solved in both Cursor and Codex.

Webinar recap: Cost Intelligence for the AI Era

CloudZero’s Umesh Rao and Larry Advey showed what it actually looks like to connect AI to real cloud cost data, and the results are hard to unsee. On April 9, 2026, CloudZero hosted a live webinar, Cost Intelligence for the AI Era, featuring Umesh Rao, Director of Enablement, and Larry “Fred FinOps” Advey, Director of Cloud Platform & FinOps.

What Is an AI SRE? And Why Do They Need Live Runtime Evidence?

AI SREs are autonomous systems that handle incident triage, root cause analysis, and remediation by correlating logs, metrics, traces, and code signals. However, as they rely on pre-configured telemetry, the critical execution details of a specific failure, such as variable state and code paths, can often be missed. As a result, they either force users into manual redeploy loops or make inferences from partial data, diagnosing issues using probability rather than proof.