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--- ‍Key Takeaways --- AI can generate code in seconds. It still can’t ship software safely. That gap isn’t about model quality or prompt engineering. It’s about context, and most software organizations don’t have a system that accurately reflects how pipelines, services, environments, policies, and teams actually relate to each other. Without that context, AI doesn’t automate delivery. It amplifies risk.

AI is changing both what you build and how you build it - at the same time. Today, Harness is announcing two new products to secure both: AI Security, a new product to discover, test, and protect AI running in your applications, and Secure AI Coding, a new capability of Harness SAST that secures the code your AI tools are writing.

This is part 1 of a five-part series on building production-grade AI engineering systems. Across this series, we will cover: Most teams experimenting with AI coding agents focus on prompts. That is the wrong starting point. Before you optimize how an agent thinks, you must standardize what it sees. AI agents do not primarily fail because of reasoning limits. They fail because of environmental ambiguity.

APIs have revolutionized how web and web app developers interact with data, whether for personal use or business. One of our most profound responsibilities as API developers is to protect our endpoints from being hacked. Even with essential safeguards in place, our websites can be vulnerable. This post discusses seven causes of API failures and how to fix them.

Speed is exposing the cracks. Our research shows that 69% of heavy AI users now face frequent deployment issues. To capture the ROI of AI, leaders must shift focus from code generation to delivery modernization. standardizing foundations and automating the "manual middle" that leads to developer burnout. Over the last few years, something fundamental has changed in software development.

Harness Database DevOps integrates Open Policy Agent (OPA) to enforce database governance through policy as code. By embedding compliance rules directly into CI/CD pipelines, teams can automatically prevent risky database changes, maintain audit trails, and meet regulatory requirements without slowing down development. Database systems store some of the most sensitive data of an organization such as PII, financial records, and intellectual property, making strong database governance non-negotiable.

The best engineering teams rely on data-driven frameworks like DORA metrics and SPACE to measure developer productivity and demonstrate business impact. This guide explores proven measurement approaches that move beyond vanity metrics to capture real engineering value and team performance. Your developer productivity initiative didn't collapse because the data was wrong. It stalled because it couldn't answer the business question. Leadership asked, "So what?".

If you've worked with builds and deployments, then you already know how central Docker images, dependencies, and containers are to modern software delivery. The introduction of Docker revolutionised how we package and run software, while the Open Container Initiative (OCI) brought much-needed standardisation to container formats and distribution. Docker made containers mainstream; OCI made them universal.

Developers and security professionals have struggled to get on the same page for what seems like forever and AI is only making that divide larger, according to results from our State of AI-Native Application Security 2025 research report.

Ahead of the DevOps Modernization Summit, Matthew Skelton, CEO & CTO of Conflux shares his takes on output-driven AI, how DORA metrics aren’t enough, and why governance and compliance must be built into the platform. ‍ Matthew Skelton is the CEO & CTO of Conflux and a featured speaker at this year’s DevOps Modernization Summit. Ahead of our annual summit, Matthew has shared his hot takes on AI, DORA, and the key to successful automation.