Operations | Monitoring | ITSM | DevOps | Cloud

The pace of AI-assisted development has outgrown how most teams actually ship. Harness is closing that gap. Engineering teams are generating more shippable code than ever before — and today, Harness is shipping five new capabilities designed to help teams release confidently. AI coding assistants lowered the barrier to writing software, and the volume of changes moving through delivery pipelines has grown accordingly. But the release process itself hasn't kept pace.

Discover why Terraform has become a vendor lock-in risk with HashiCorp's licensing changes. This guide explores practical strategies to escape Terraform lock-in, including OpenTofu migration paths and how Harness IaCM enables vendor-neutral infrastructure management across multiple IaC tools.

The catastrophic TeamPCP exploit in March 2026 demonstrated that "open execution" models, in which third-party code runs with full privileges, have made CI/CD pipelines a primary target for global credential harvesting. There are better architectures. On March 19th, the risks of running open execution pipelines — where what code runs in your CI/CD environment is largely uncontrolled — went from theoretical to catastrophic.

Here's a scenario that probably sounds familiar: a developer needs a sandbox environment to test something. They file a ticket. Then they wait. And wait. Maybe a day goes by, maybe three. Meanwhile, your platform team is buried in provisioning requests, and somewhere, someone has already spun up an unsanctioned workaround that bypasses every governance policy you've put in place. It's a lose-lose. Developers lose velocity, platform teams lose their sanity, and security gaps quietly multiply.

This comprehensive guide covers the fundamentals of load testing, key differences from stress and performance testing, step-by-step execution methods, popular tools, and best practices to help teams build resilient systems with confidence. In today's always-on digital economy, a single slow page or unexpected crash during peak traffic can cost businesses thousands or even millions of dollars in lost revenue, damaged reputation, and frustrated customers.

Taking a look back over the last ten years in enterprise technology, paradigm shifts are occurring more frequently. For example, the maturity of DevOps/Platform Engineering and Cloud Native infrastructure has occurred. The new frontier depending where you are in adoption is AI. As your adoption and maturity curve progress, operationalizing these paradigms become important.

For the past few years, the narrative around Artificial Intelligence has been dominated by what I like to call the "magic box" illusion. We assumed that deploying AI simply meant passing a user’s question through an API key to a Large Language Model (LLM) and waiting for a brilliant answer.

On March 24, 2026, the AI open-source ecosystem was impacted by a critical supply chain attack involving the widely used Python package LiteLLM. Attackers compromised the LiteLLM PyPI distribution pipeline and published malicious versions (notably in the 1.82.7-1.82.8 range), embedding a multi-stage payload designed to steal credentials and execute remote code.

You're tagging Docker images with build numbers. -Build is your latest production release on main. A developer pushes a hotfix to release-v2.1, that run becomes build. -Another merges to develop, build. A week later someone asks: "What build number are we on for production?" You check the registry. -You see,,, on main. The numbers in between? Scattered across feature branches that may never ship. Your build numbers have stopped telling a useful story.

--- Key Takeaway: Harness AI helped deflect 95% of the platform support tickets for a major financial institution --- These days, success is often measured by what doesn’t happen: When things go right, the software delivery platform is invisible. But what happens when an organization’s delivery velocity increases multifold? Can the platform still stay out of the way?

Modern engineering teams run on CI/CD. It’s where pull requests get validated, artifacts get produced, and releases get promoted to production. That also makes CI/CD migration very risky because you're not just moving a "tool"; you're moving the workflow that developers use dozens or hundreds of times a day. The good news: disruption is optional.

Modern software teams are under constant pressure to ship faster without breaking production. That’s why CI/CD best practices have become essential for high-performing DevOps organizations. Continuous integration and continuous delivery (CI/CD) help automate builds, testing, and deployments — but simply installing a pipeline tool isn’t enough. Without the right practices, pipelines become slow, flaky, and difficult to govern.

‍Flaky tests are automated tests that pass or fail inconsistently without changes to the code. In this guide, you’ll learn why flaky tests happen, how to detect them automatically in CI pipelines, and how modern platforms prevent them from slowing teams down. Your test went well three times yesterday. It didn't work this morning. You ran it again without changing anything, and now it works. Congratulations, you've just passed a flaky test, and now someone's day is going to be ruined.

Most engineering teams know the difference between “we have tests” and “we know we’re well-tested.” Your CI builds may be green, but without code coverage, it’s hard to prove how much of your code is actually exercised by automated tests. Code coverage measures what percentage of your code runs during tests (lines, branches, and functions), and when you wire it into CI gates, it becomes an enforceable quality signal and not a vanity metric.

Internal platform adoption usually doesn’t fail because developers “hate standards.” It fails because the platform doesn’t make their day easier. If your portal still means waiting, waiting on an environment, waiting on an approval, waiting on the platform team, it becomes one more tab that people stop opening. But if the platform lets engineers get the common stuff done quickly (with guardrails that keep things consistent), they’ll come back on their own.

A DevOps pipeline is a critical part of modern software delivery. It is a series of automated steps that move code from commit to production quickly, reliably, and consistently. At its core, a DevOps pipeline is a system that helps teams build, test, and release apps in an easier way. It cuts down on manual work and mistakes. This helps teams send out updates more often, make better software, and react quickly when the business needs change.

At SREday NYC 2026, the ShipTalk podcast welcomed Birol Yildiz, Co-founder and CEO of ilert, for a conversation about the next evolution of incident response. In the episode, ShipTalk host Dewan Ahmed, Principal Developer Advocate at Harness, spoke with Birol about how artificial intelligence is transforming reliability engineering—from simply assisting engineers during incidents to autonomously diagnosing and resolving outages.

‍ We've all been there. You push a PR, grab coffee, check Slack, maybe start a side conversation — and your build is still running. Multiply that across a team of 50 engineers, and you're looking at hours of lost focus every single day. Slow CI/CD builds don't just waste time. They generate a steady stream of "CI is slow" tickets that eat into your platform team's roadmap. Intelligent caching is one of the fastest ways to break that cycle.

Definition: Parallel execution in CI is the practice of running independent build, test, or deployment tasks concurrently to reduce feedback time, improve resource utilization, and control infrastructure costs. Developers often spend almost half their time waiting for builds that could be faster. Simply adding more resources is not enough. Real improvements come from planned parallelism, using concurrency together with test intelligence, caching, and strong governance.

Definition: CI pipeline optimization is the practice of reducing build and test time and the cost per build by running only what matters, reusing unchanged components, and enforcing standardized governance. Platform teams are wasting thousands of hours every year because their pipelines aren't working right. Developers wait 45 minutes for builds. Jenkins consumes 20% of your team's capacity on maintenance.

-- Key Takeaways: The Harness MCP server is an MCP-compatible interface that lets AI agents discover, query, and act on Harness resources across CI/CD, GitOps, Feature Flags, Cloud Cost Management, Security Testing, Resilience Testing, Internal Developer Portal, and more. -- The first wave of MCP servers followed a natural pattern: take every API endpoint, wrap it in a tool definition, and expose it to the LLM.

E2E Testing Has a New Bottleneck, and It's Not the Code End-to-end (E2E) testing has always been the hardest part of a QA strategy. You're simulating real users, navigating real flows, validating real outcomes across browsers, environments, and data states that never hold still. Traditional test automation tackled this with scripts: rigid, deterministic sequences tied to element selectors and hard-coded values. They worked until the UI changed. Or the data changed.

Outages in distributed systems are inevitable, making resilience testing essential in the SDLC. It must be continuous, covering failures, load, and disasters. Delayed validation creates “resilience debt,” increasing risk. A holistic approach—combining chaos, load, and DR testing—plus cross-team collaboration and AI-driven insights improves reliability and reduces impact. Modern software delivery has dramatically accelerated.

--- ‍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.

Modern database design is no longer a one-time activity but an ongoing process that evolves as business needs, scale, and system behavior change. Instead of large redesigns, teams rely on incremental and backward-compatible schema changes, such as adding columns, indexes, or new tables, to safely adapt the database without disrupting production.