Operations | Monitoring | ITSM | DevOps | Cloud

Reduce CI Costs Without Slowing Down Development | Harness Blog

Continuous integration (CI) costs can escalate quickly as engineering teams scale. While most organizations focus on cloud bills, the true cost of CI includes slow build times, developer wait time, inefficient test execution, and overprovisioned infrastructure. CI cost optimization is the practice of reducing the total cost of CI pipelines by improving build efficiency, minimizing compute usage, and eliminating unnecessary work without slowing down development.

Why Artifact Repository Sprawl Slows Down Software Delivery | Harness Blog

Three weeks into a platform modernization project, this question landed in my inbox: "Why does our deployment pipeline take 40 minutes instead of four?" This is artifact repository sprawl in practice, and it does more than slow pipelines. It fragments your security posture, your compliance evidence, and your ability to answer basic questions like "what's actually running in production right now?".

Mini Shai-Hulud Explained: How the TanStack and RubyGems Supply Chain Attacks Worked | Harness Blog

Shai-Hulud is back - this time being lighter, faster and more automated than before. This new wave, termed as Mini Shai-Hulud, has affected a number of packages from tanstack, uipath, opensearch-project and mistralai among others over the past few weeks, with the latest series of major compromises coming on 19th May, 2026 on major organizations openclaw-cn and antv. Check an extensive list of affected packages here.

What a Context Graph Actually Is, and How to Build One | Harness Blog

Engineers have been shipping pieces of "the graph" for years. Service maps. Dependency graphs. Knowledge graphs. RDF triples. The newest entrant is the context graph, and the reason it shows up now is specific: software is increasingly executed by agents, and agents need a model of how work actually happens, not just an index of what exists.

Core Java vs Enterprise Java: Jakarta EE, Spring Boot & Modern Trade-offs [2026 Guide] | Harness Blog

‍ When you're architecting an enterprise Java application, one decision quietly shapes everything downstream: runtime footprint, deployment pipelines, and how your platform team handles incidents at 3 a.m. For two decades, that decision was framed as Java SE vs Java EE. In 2026, that framing has quietly inverted.

AI, Platforms, and the Future of Value Delivery: A Conversation with ServiceNow

How do enterprises turn AI from experimental potential into real-world software delivery value — without slowing down, breaking security, or sacrificing reliability? At {unscripted} 2025, Amit Zavery — President, Chief Product Officer, and COO of ServiceNow — joined Harness CEO and Founder Jyoti Bansal for a candid fireside chat on the future of AI in the enterprise, the role of platforms in unlocking developer productivity, and why"AI-native" only works when speed, security, and reliability move together.

Automated Release Management: From CABs to Continuous Delivery | Harness Blog

The thing with Change Advisory Boards is that the intent was always good. Get smart people in a room, look at the evidence, and make sure nothing catastrophic goes out the door. In theory, that's hard to argue with. It doesn't scale in practice. Things happen between meetings. Teams rush to hit the window. The CAB meeting may not catch every risky deployment, but at least everyone can feel good about the process before the incident happens. Automated release management asks a different question entirely.

AI Asked Our General Counsel Anything. She Didn't Hold Back.

What happens when AI interviews a tech leader? You get unexpectedly honest answers. Harness General Counsel Hanna Steinbach sat down with ChatGPT — and skipped the corporate script. From the realities of parenting while leading a legal team at a high-growth startup, to the daily habits that keep her grounded, this is the kind of candid leadership perspective you rarely see. Oh, and she's definitely the person sprinting to the gate right as boarding starts.

Disaster Recovery Testing: A Practical Step-by-Step Guide for 2026 | Harness Blog

Effective disaster recovery testing follows a clear three-phase lifecycle: plan, execute, and review. Most DR programs fail not because of missing tools, but because of untested runbooks and unclear ownership. Platforms like Harness Resilience Testing bring chaos, load, and DR testing into one pipeline so teams can catch risks before they become incidents. Most organizations don't fail at disaster recovery because they lack technology.

The AI Productivity Paradox: We're Measuring the Gains and Missing the Costs | Harness Blog

For the past year, I've been hearing a version of the same thing from engineering leaders: AI tools are working, productivity is up, the business case is there. And yet, something about the picture still feels incomplete. So we decided to go find out how widespread that feeling actually is. We surveyed 700 engineers and managers across five countries, and published the results in the State of Engineering Excellence 2026.

SLI, SLO, SLA: What They Mean for Load Testing

Most engineers can recite these three terms. Fewer know how they actually connect during a load test. If your team is running performance tests without mapping results to SLOs, you're collecting data without a pass/fail signal. This short gives you the mental model to turn load test output into something your SLA can actually depend on.

Backup vs Disaster Recovery: They're NOT the Same Thing | Resilience Testing | Harness

Having backups doesn't mean you have disaster recovery. And that gap could kill your business. Backups are just data snapshots stored safely for restoration when files get corrupted or deleted. Disaster recovery is your complete operational playbook for bringing back servers, applications, networks, and entire infrastructure after catastrophic failures. You can restore every byte of data from backup and still watch your business stay offline for hours or days because you lack the recovery procedures, failover systems, and tested runbooks to actually get operations running again.

Q1 2026 Product Update: Harness Pipeline | Harness Blog

The first quarter of 2026 introduces eight major pipeline orchestration enhancements that accelerate development, simplify validation, and strengthen governance. Execute pipelines from Git tags for immutable versioning, leverage AI to author OPA policies without Rego expertise, and gain complete visibility into queued pipelines across your account.

Q1 2026 Product Update: Harness Continuous Delivery & GitOps | Harness Blog

The first quarter of 2026 introduces AI-powered continuous verification that eliminates configuration overhead, expanded deployment platform support including Azure Container Apps and enhanced Windows capabilities, and GitOps workflow improvements that align with how teams actually ship software.

Introducing Harness Release Orchestration: Enterprise Release Management, Reimagined | Harness Blog

Enterprise releases spanning multiple services, teams, and environments demand more than spreadsheets and manual coordination. Harness Release Orchestration provides a unified framework for modeling, automating, and tracking complex releases with complete visibility from planning through production deployment.

Harness Lives Inside Cursor Now - Plus Everything Else That Shipped in April

April was a big month at Harness. AI is changing how code gets written — and the rest of the SDLC is catching up. In this update, Dewan Ahmed walks through Harness product releases across three themes: AI in the developer workflow, security and governance for AI assets, and self-service maturity for developers and platform teams. What's covered (with timestamps): Found this useful? Subscribe for monthly product updates, and drop a comment telling us which release you want a deep dive on next.

Learn these 4 Chaos Engineering Principles Before You Break Anything | Resilience Testing | Harness

Want to start chaos engineering? Don't randomly break stuff and hope for the best. Real chaos engineering starts with defining your system's steady state metrics like latency, throughput, and error rates. Then you form a clear hypothesis about what should happen when failures occur. Next, you inject controlled failures, starting small with single pod kills or network drops, not production meltdowns. Finally, you limit the blast radius by running experiments in safe environments first.

The most debated DORA metric (even Google debates this)

What's the most debated DORA metric? Nathen H from Google's DORA team breaks down the change lead time debate — and why even the experts can't fully agree on when a change is "committed." Is it at commit? After merge? The answer matters more than you think. Subscribe for more DevEx and DORA insights from our Web Summit series.

AI in Software Delivery: Engineering Excellence or Just Market Hype? | Harness Blog

AWS re:Invent 2025 made one thing very clear: enterprise interest in AI is no longer theoretical. The conversation has moved beyond curiosity. Teams are actively experimenting, leaders are looking for production-ready use cases, and engineering organizations are trying to figure out where AI can create real leverage across software delivery, security, platform engineering, and operations.

Get Ship Done: Everything We Shipped in April 2026 | Harness Blog

It’s becoming increasingly clear that AI-generated code can create real challenges once it reaches production. At Harness, we’ve been focused on innovating fast and solving those problems, so teams can move quickly without sacrificing reliability. In the past 30 days, we delivered 70+ new features.

Google Cloud Next '26 Recap: AI, Efficiency, and the Rise of Frictionless Delivery | Harness Blog

‍Summary: Google Cloud Next ’26 focused on the future of software delivery, emphasizing that AI, platform consolidation, and an urgent push toward efficiency are reshaping the Software Development Life Cycle (SDLC). The key takeaway from the event was that organizations are moving from AI experimentation to operationalization, actively consolidating fragmented tools onto end-to-end platforms that embed AI for control, intelligence, and speed. ‍