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Resilience for an AI-Powered Future: PagerDuty's FY26 Impact Report

The impact vision for PagerDuty.org is to enable mission-driven teams to build a resilient world and a sustainable future for all. As a leader in modern, AI-first operations, we know that operational excellence supercharges social impact. As artificial intelligence rapidly reshapes the social sector, this commitment to resilience and efficiency has never been more vital.

Scout MCP Server: Example Prompts, Use Cases, and What's New

The Scout MCP server connects your AI assistant directly to your Scout Monitoring data. Instead of switching between your editor, Scout, and a chat window, your assistant can pull traces, errors, N+1 insights, and endpoint metrics on its own and use that context to suggest or make fixes right in your codebase. This covers how to connect it, what to ask it, how other teams are using it, and what we shipped recently.

Why AI observability is a critical ITOps priority

AI Observability is a Critical Priority for ITOps Teams See how LogicMonitor helps ITOps teams monitor AI workloads, reduce blind spots, and move toward Autonomous IT. Schedule a meeting AI has shifted from experimental pilots to everyday business operations. Customers are interacting with AI-powered applications. Engineering teams are building with LLMs, GPUs, APIs, and automation at a much faster pace. That adds to the visibility strain on already overburdened ITOps teams.

How one PM scaled customer discovery with AI

Customer interviews are one of the most powerful ways to build better products — but they’re also time-consuming. In this video, Avinoam “Avi” Zelenko, Principal Product Manager at Atlassian, shares how he transformed the way he runs customer interviews using AI automation and Rovo agents. What used to take hours of coordination, note-taking, and manual summaries now happens automatically. By stitching together the Teamwork Collection and Slack, Avi built a workflow that captures conversations, summarizes insights, and shares them across teams in real time.

Why Multi-Agent AI Workflows Need a Control Plane

AI is transforming how infrastructure and platform teams design, deploy, and operate systems. As organizations move from experimentation to production, a clear pattern is emerging. AI can decide what should change, but it cannot safely control how those changes are executed. This creates a gap in modern architectures. That gap is filled by a control plane. That control plane already exists in Puppet Enterprise Advanced.

AI Agents Are the New Employees: The Identity & Security Crisis Enterprise IT Must Solve

As AI agents become more autonomous, enterprises face a new challenge: How do you secure a workforce that isn't human? In this episode of Agents of IT, Fran Fernandez, Zach Austin, and Ian Coppock explore the growing identity and security challenges surrounding Agentic AI. From permissions and governance to digital identities and access controls, the team breaks down what enterprise leaders need to know before deploying AI agents at scale.

Why We Built Lynx: Bringing Control to the Age of AI Agents

For a decade, one idea has guided everything we’ve built at Tigera: How do you secure a dynamic system with a lot of moving parts that is changing rapidly, with a programmatic approach? Calico has applied that idea for Global 2000 companies running the largest Kubernetes platforms in the world, securing tens of millions of mission-critical transactions every day. Today I’m excited to announce the next chapter of that work: Lynx, a unified control plane for Kubernetes-native AI agents.