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Key Takeaway: The Harness MCP Server is now in the official Claude Connectors Directory. Developers using Claude can now discover and connect to Harness, gaining structured, real-time access to their pipelines, deployments, approvals, and delivery workflows. What makes this different from a typical API integration is what's underneath: the Harness Software Delivery Knowledge Graph, which gives Claude the context it needs to make decisions that are accurate, fast, and safe. ‍

Modern data platforms are evolving rapidly, and Google Cloud BigQuery has become a core part of analytics, AI, and large-scale reporting architectures. Teams (including Harness) rely on BigQuery to process and analyze massive datasets, but managing schema changes in a secure, repeatable way can still be challenging.

When Anthropic broke the news of Mythos and Project Glasswing, the security community did what it always does. It published a flurry of papers asking "What does this mean for security?" It's a reasonable instinct, but it's the wrong question. The real question is who actually owns the problem?

Releasing new software used to be a big deal. You would set aside a Saturday night, wake up the on-call engineer, push the code, and hope that nothing broke before Monday morning. Then came feature flags, which changed everything without anyone noticing. Feature flags let you separate deployment from release, so you can send code to production in a dormant state and turn it on for users when you're ready. No more 1 a.m. maintenance windows.

AI coding tools made code generation faster. Measuring what actually ships is the hard part. Over the last eighteen months, tools like Cursor, Claude Code, Copilot, and Windsurf have fundamentally changed how software gets built. AI-generated pull requests are increasing, developers are producing more code than ever before, and workflows that once took hours now happen in minutes. But most organizations struggle to clearly explain what that investment is actually producing.

Gartner expects worldwide AI software spending to hit $2.59 trillion in 2026, 47% more than organizations spent last year. The dollars are real and growing fast. But most organizations still can't measure the ROI of that spend. The problem has two sides: developers and infrastructure. On the developer side, engineers are using AI to write nearly every line of new code, and leaders have no way to tell whether that spend is producing software that ships.