Operations | Monitoring | ITSM | DevOps | Cloud

AI Assistant for Calico: Troubleshooting at the Speed of Thought

Despite the wealth of data available, distilling a coherent narrative from a Kubernetes cluster remains a challenge for modern infrastructure teams. Even with powerful visualization tools like the Policy Board, Service Graph, and specialized dashboards, users often find themselves spending significant time piecing together context across different screens.

What Engineers Want from AI in Observability... According to the 2026 Observability Survey Report

The results show strong interest in AI for forecasting, root cause analysis, onboarding, and generating dashboards, alerts, and queries. But when it comes to autonomous action, practitioners are more cautious — and 95% say AI needs to show its work to earn trust.

The Hidden Failure Points in Your AI Strategy

New models, new agents, new capabilities. It seems like every week there’s a new must-have AI function. It’s no surprise that leaders are feeling pressure to move quickly. At a PagerDuty on Tour event, a customer joked that they couldn’t fathom having a five-year AI strategy; it makes way more sense to have a five-minute one. There’s truth in that comment.

What's New in Turbo360 - AI agents for Azure cost optimization, Azure cost pulse summary report...

Turbo360 brings a suite of enhancements added to elevate your Azure management experience. Hit play to hear what's in store for this month. 00:00:00 - Intro 00:00:13 - Cost Pulse Summary Report 00:00:49 - Configuring Cost Pulse Summary 00:01:17 - New AI Agents (4 New Agents) 00:01:54 - Accessing AI Agents 00:02:18 - Related Resources Feature 00:02:40 - Budget Planner 00:02:59 - Setting Up Budget Planner Permissions 00:03:11 - Multi-Subscription Onboarding 00:03:43 - AI Agents Role-Based Access 00:04:10 - New RA-GRS Optimization Recommendation 00:04:30 - Summary & Call to Action.

The Art of Prompting in AI Test Automation | Harness Blog

E2E Testing Has a New Bottleneck, and It's Not the Code End-to-end (E2E) testing has always been the hardest part of a QA strategy. You're simulating real users, navigating real flows, validating real outcomes across browsers, environments, and data states that never hold still. Traditional test automation tackled this with scripts: rigid, deterministic sequences tied to element selectors and hard-coded values. They worked until the UI changed. Or the data changed.

What are test hooks in AI-native development?

Summary: A test hook connects a test or lint command to an event in your AI coding agent’s workflow. When the event fires, the agent runs the command automatically. If it fails, the agent’s action is blocked. You can wire your existing test commands into your agent’s lifecycle hooks to get deterministic local validation before code ever reaches CI. AI coding agents write code at a pace where stopping to manually run tests breaks your flow.

AppSignal's MCP Server: Connect AI Agents to Your Monitoring Data

Your AI coding assistant already knows your codebase. Now it can know your production environment too. AppSignal's MCP server gives AI agents and AI code editors direct access to your monitoring data — errors, performance metrics, and more — so they can help you debug, investigate and resolve issues without switching context. And with our new public endpoint, getting started is simpler than ever.