Operations | Monitoring | ITSM | DevOps | Cloud

TLDR: When you work in Harness AI Chat, your work doesn't stop at Harness. Your pipelines live here, but the change you actually need to make might be a YAML file in GitHub, a Jira ticket, or a Confluence doc. So we built an MCP Gateway inside Harness that lets AI Chat reach those third-party apps for you: safely, under Harness's own access controls and secrets, and without dropped sessions across our distributed fleet. This is the story of what we built and why.

DevOps is a culture and practice that gets development and operations teams to collaborate, automate, and ship software faster and more reliably. Platform engineering is the discipline that builds the internal tooling and self-service infrastructure that makes those DevOps practices repeatable at scale. Put simply: DevOps is the goal; platform engineering is one of the most effective ways to reach it across many teams. Your developers are shipping code faster than ever.

A software delivery platform is an integrated system that manages every stage of moving software from a code commit to production: continuous integration, continuous delivery, security, and the feedback loops in between. It treats delivery as one governed lifecycle instead of a chain of disconnected steps.

Traditional FinOps focuses on cutting overspend, but the real opportunity lies in maximizing savings you're missing. This paradigm shift reframes cloud cost management as a proactive savings optimization strategy rather than reactive spend control, helping organizations unlock hidden cost efficiency through governance, automation, and continuous optimization practices. Cloud spend is up 40% year-over-year. Your CFO wants answers.

Manual intake eats the first 10 to 30 minutes of an incident. Harness AI SRE runs it as automation and gives those minutes back to your responders.

Here's the uncomfortable truth about the Mythos era: knowing about a vulnerability and being able to neutralize it are two entirely different problems. AI models like Mythos are finding vulnerabilities 10x faster than humans ever could. Project Glasswing participants discovered over 10,000 high and critical vulnerabilities in their applications. Firefox alone had 271 previously unknown zero-days exposed by Mythos. That's the good news.

At Harness, we build an AI-powered software delivery platform, and test result data is core to how we help engineering teams ship faster. The table that stores it started small: one row per record, all the context right there on the row. Simple, readable, and it worked. Until it didn't. This is the story of how we refactored it, what we learned, and what I'd tell you to watch for in your own systems.

The latest Resilience Testing documentation update brings Chaos Hub directly into the docs, making it easier to discover and use fault, probe, and action templates. It also introduces a Prompt Library with ready-to-use AI prompts for Harness MCP, helping teams run resilience workflows faster using natural language.

AI has fundamentally changed software development. Developers are writing more code than ever. AI coding assistants can generate features, tests, documentation, and infrastructure configurations in minutes. Engineering organizations are seeing meaningful productivity gains as AI becomes embedded throughout the software development lifecycle. But there is a catch. Security teams now face a difficult reality: application security was already struggling to keep pace with software delivery before AI arrived.

Cloud cost visibility at scale usually works great… until it suddenly doesn’t. At first, everything feels manageable. You can track spend by service. You know which team owns which resources. Reports are clean, and the numbers make sense. Then one day, there’s a $47,000 spike spread across three AWS accounts that no one noticed for eleven days. Leadership wants answers. Engineering wants context. And your carefully designed tagging strategy?