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Get more value out of your Cortex catalog with our MCP prompt library

You've set up the Cortex MCP and connected it to your AI assistant and IDE. You ask about service ownership, check a Scorecard or two, and it works. You're impressed by how much faster this is than clicking through the web UI. Now you're wondering what else you can do with it. I'm willing to bet we've hit a nerve with that "hypothetical" scenario. The Cortex MCP works exactly as designed, but it's deceptively difficult to know which questions to ask and when to ask them.

AI-Powered Observability: From Reactive to Predictive

If there’s one thing clear from our AI-powered observability webinar, it’s that observability has officially graduated from a “nice-to-have” to a business-critical discipline, and AI is helping lead that charge. Our webinar brought together guest speaker Stephen Elliott, Group VP at IDC, and Ranbir Chawla, former SVP of Engineering at RB Global, for an hour of insights that mixed data, experience, and hard-won lessons from the trenches.

Why UX is the Missing Layer in AI Adoption And How to Fix It

Most AI programs don’t fail on model quality. They fail because the experience makes people either over-trust or quietly avoid the system. Employees often use AI more than leaders realize, frequently without training or guardrails. Interfaces that just “show an answer” without confidence, provenance, or recourse create two risks: blind reliance and shadow use.

Accelerating Our Mission to Bring AI to Everything After Code

Since launching Harness in 2017, we’ve been on a mission to unlock faster innovation by removing the bottlenecks that slow software engineering teams down. From day one, we believed that the biggest obstacles in engineering weren’t in writing code — they were in everything that followed.

The AI Cost Crisis: 'AI Cost Sprawl' Is Crashing Your Innovation (AI Cost Sprawl Explained + How To Fix It)

AI should speed up innovation, not inflate your cloud bill. But today, the biggest GenAI challenge for SaaS teams isn’t model quality; it’s cost. And increasingly, that cost comes from AI cost sprawl. That’s not because anyone is doing something wrong, but because AI operates differently from the cloud services we’ve all spent a decade learning how to manage.

Capture and Use Network Response Data in AI Powered Testing

Learn how to capture and use response data from network calls to build smarter and more reliable AI-driven tests. This walkthrough covers the full workflow from configuring user actions to extracting backend responses, validating data, and creating dynamic test flows. You will also see how response data improves debugging visibility and supports data-driven automation. The video includes Ideal for developers, testers, and platform engineers looking to improve the accuracy and resilience of AI-powered test suites.

How to Build a Clear AI Implementation Strategy

Organizations see AI’s transformative potential, but success requires more than technology – it demands a clear strategy led by IT. A structured AI implementation roadmap aligns initiatives with business goals, establishes governance, and enables measurable ROI, while improving employee and customer experiences. Yet, 66% of organizations view AI as critical, but only 38% report meaningful competitive advantage, highlighting the need for disciplined adoption.

Planning a Smooth Cutover When You Change Critical Business Tools

A cutover is when you change from an old IT system to a new one. With technology advancing at a rapid rate, more and more businesses are learning about and implementing cutovers. If now's the time for you to execute a cutover, it's important that you plan everything effectively so that it goes smoothly. Planning a smooth cutover is easier said than done, however. There are some important things you need to know first. Until you conduct extensive online research, you're never going to be able to effectively plan a cutover.