Operations | Monitoring | ITSM | DevOps | Cloud

AI for Everything After Code: Ship Fast, Stay Safe

Recorded at @DevOpsLive Most teams have “done DevOps” and “built a platform,” but still wrestle with the same core problems: platforms that developers dodge, AI that accelerates coding while quietly degrading delivery performance, security and compliance that can’t keep up, cloud bills that keep climbing, and incident response that hasn’t caught up with cloud‑native complexity.

Why Release Management Is Broken and How to Fix It

Are you tired of slow, expensive, and ineffective Change Advisory Board (CAB) meetings? In this video, Eric Minick from Harness explores the evolution of release management and how to transition from traditional manual approvals to a streamlined, automated DevOps approach. What You'll Learn: Whether you are a release manager or a DevOps engineer, learn how to build a reliable audit trail while accelerating your software delivery.

Women in Tech: Journeys, Grit, and the Future We're Building | Harness Blog

Technology evolves rapidly — but progress in tech isn’t driven by tools alone. It’s driven by people. By curiosity. By courage. By individuals who choose to step into complex systems and shape how they function. As an engineering leader driving application and API security, I have always believed that our industry is at its best when complex concepts are made accessible and practical for everyone.

AI vs. Hype: Redefining Engineering Excellence with Ron Miller

In this episode of "ShipTalk: Engineering Excellence," host Thomas Dockstader sits down with Ron Miller, editor at Fast Forward, to discuss the real-world impact of AI on software development. They dive deep into the maturity of AI-driven code, the rise of the "citizen developer," and why traditional writing and communication skills are becoming the new must-have for modern engineers.

Site Reliability Engineering (SRE) 101: Everything You Need to Know | Harness Blog

A single second of latency can cost e-commerce sites millions in revenue, while just minutes of downtime trigger customer churn that takes months to recover. Modern users expect instant responses and seamless experiences, making reliability a competitive feature that directly impacts business outcomes. Site Reliability Engineering treats operations as a software problem rather than a manual discipline. SRE applies engineering principles to achieve measurable reliability through automation.

Your AI Agents Are Only As Good As Your Data | Harness Blog

Every agent demo follows the same arc. The agent calls an API. A deployment triggers. A ticket gets created. The audience is impressed. Then someone asks a real question: "Which regions had the highest order failure rate this quarter, and are any of them linked to vendor SLA breaches?" That question crosses four entity types — orders, fulfillment records, vendors, SLA contracts.

Building Governance, Auditability, and Visibility into Database DevOps | Harness Blog

Database changes are inherently complex: coordinating schema updates, managing risk, and avoiding downtime all require care. Even when teams improve how they deliver those changes, governance often remains inconsistent, manual, and reactive. In many environments, governance is treated as a separate layer around deployment. Policies are applied unevenly, approvals become bottlenecks, and audit evidence is assembled after the fact, creating gaps in enforcement and increasing operational risk.

Why DR Testing Can No Longer Be an Afterthought | Harness Blog

Regular DR testing is no longer a compliance checkbox — it is a critical engineering discipline that determines whether an organisation can survive a real cloud outage with its services and revenue intact. As the AWS Middle East incident demonstrated, regional cloud failures can strike without warning and defeat standard redundancy models, making untested DR plans dangerously unreliable.

Unlocking Security Potential for AI: Introducing the Harness WAAP MCP Server | Harness Blog

Security teams face overwhelming amounts of data and complex interfaces, making it hard to access critical insights. AI tools promise solutions, but integration remains difficult as time ticks away and leadership wants the latest data to inform risk decisions. Most security platforms lack seamless integration, slowing access to important data and hindering AI-powered workflows.

Testing AI with AI: Why Deterministic Frameworks Fail at Chatbot Validation and What Actually Works | Harness Blog

Chatbots are becoming ubiquitous. Customer support, internal knowledge bases, developer tools, healthcare portals - if it has a user interface, someone is shipping a conversational AI layer on top of it. And the pace is only accelerating. But here's the problem nobody wants to talk about: we still don’t have a reliable way to test these chatbots at scale. Not because testing is new to us. We've been testing software for decades.