|
By Vikas Gautam
Harness AI Security provides a unified control plane for AI discovery, risk visibility, and runtime protection, helping organizations operationalize key requirements of the EU AI Act. Instead of relying on manual audits or fragmented tooling, teams get continuous insight into how AI systems are built, exposed, and used, along with the evidence needed to demonstrate compliance.
|
By Jyoti Bansal
AI is writing more of the code. Software delivery, the work between writing code and running it in production, is where most of the day still goes. Building, testing, scanning, deploying, remediating, and operating still require the same, if not more, effort as before AI. Today, we're introducing Autonomous Worker Agents for software delivery: the platform for enterprises to build and safely run AI agents that handle the work between writing code and shipping it to production.
|
By Rohan Gupta
The Harness MCP Server now connects directly inside Google Antigravity. Developers can link Harness in under two minutes and give the agent structured, real-time access to their pipelines, execution history, services, environments, and policies, without leaving the editor. What makes it reliable isn't the connection itself. It's the Harness Software Delivery Knowledge Graph underneath, which gives the agent the context to act accurately, fast, and within your guardrails.
|
By Shibam Dhar
AI coding assistants accelerate development but can rapidly introduce vulnerable, malicious, or non-compliant open-source dependencies into your codebase. Harness Artifact Registry's Dependency Firewall acts as a registry-level control point, evaluating and blocking risky external packages before they enter your CI/CD pipeline—essential protection against modern npm-style supply chain attacks.
|
By Abhay Ganvir
When a CI pipeline runs on cloud infrastructure, the build machine is ephemeral. It spins up, executes your build, and disappears. During that window, you have zero visibility into how much CPU and memory your pipeline actually consumes. This blind spot creates real problems. Teams over-provision VMs "just in case," wasting compute spend. Others under-provision and deal with silent OOM-kills or CPU throttling — the only clue being a cryptic exit code 137.
|
By Harness Team
Harness has been recognized as a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms for the third consecutive year. Harness was also positioned furthest on the Completeness of Vision axis in the report. Harness has been recognized as a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms for the third consecutive year. Harness was also positioned furthest on the Completeness of Vision axis in the report.
|
By Eric Minick
For Platform Engineering teams, the goal has always been clear: build a secure, scalable internal developer platform that reduces cognitive load and accelerates time-to-market. Yet, a massive obstacle often remains hidden in plain sight: the mainframe. While your distributed teams are shipping cloud-native microservices multiple times a day, your core backend mainframe applications frequently remain locked in an isolated silo, lagging behind on slow monthly or quarterly cadences.
|
By Chinmay Gaikwad
The Harness VS Code Extension is now on the Marketplace. Monitor pipelines, debug logs, approve deployments, and query failures with Claude Code, Copilot, or Cursor, without leaving VS Code. Your Harness pipelines, logs, and deployment approvals are now a sidebar panel away inside VS Code. The Harness VS Code Extension is live on the VS Code Marketplace today, no.vsix download, no manual install.
|
By Chinmay Gaikwad
Learn how to master Azure deployment with CI/CD pipelines, progressive delivery, and feature flags. See how Harness helps engineering teams ship faster and safer on Azure. Azure deployment sounds straightforward. Push code, it runs in the cloud. But if you've managed a 2 a.m. production incident because a deployment went sideways on AKS, you know the gap between "it deploys" and "it deploys safely at scale" is significant.
|
By Roshan Piyush
The Shai-Hulud lineage has a new face. On June 1, 2026, security teams independently flagged a fresh supply chain compromise inside the @redhat-cloud-services npm namespace. 32 packages and 96 versions were all republished with a credential-stealing worm. These aren't typosquats. They are the official packages in a trusted scope, pulling somewhere 80,000-117,000 average weekly downloads.
|
By Harness
Today, we're launching Autonomous Worker Agents, AI agents that run as governed pipeline steps inside Harness. They inherit OPA policies, RBAC, audit trails, and scoped credentials from the first run. And because they live inside your Harness pipelines, they reason using the Harness Knowledge Graph: your services, deployments, incidents, and policies.
|
By Harness
Engineering teams are burning through AI budgets with nothing to show for it — $100M across 10,000 engineers and no cost per run, no cost per outcome, just a number that keeps climbing. When it runs dry, your infrastructure upgrade gets cut. Harness ties every AI token to the outcome it created: cost per run, cost per resolved ticket, and anomaly detection before the invoice hits. One customer went from a $28,000 black box bill to $0.60 per ticket.
|
By Harness
The era of token maxing is over. When Claude Fable 5 launched last week at $10/$50 per million tokens - double the price of Opus 4.8 - it was a clear reminder that the most powerful model isn't always the right model. Not every task needs the Ferrari. The fastest way to burn your Al budget is sending every request to the most expensive model by default. The real question for the next phase of Al cost management isn't "can this model do the job?" — it's "is it the right model for the job?".
|
By Harness
Harness has been named a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms — for the third year in a row. Here's what stands out: almost every company in the Leaders quadrant is missing a piece. Some have Dev, some have Ops, but not the security. Harness is the one platform that brings Dev, Sec, AND Ops together — with AI built in. AI is changing how software gets built: more code, more automation, more agents — but also more complexity. Teams need delivery, security, testing, reliability, and cost in one place. That's what we're building every day.
|
By Harness
Will AI replace engineers? Hugo Charré from Cursor argues the opposite. In this clip from the upcoming summit, Hugo Charré (Cursor) explains why the number of engineers is going to grow as we scale agentic systems — and why AI raises the bar for who's on the team.
|
By Harness
Your entire AI stack ran on a model that disappeared in three days. The US government issued a directive suspending all access — a few hours' notice, no deprecation window, no roadmap. Launched Tuesday. Gone by Friday. And every enterprise that had built workflows on top of it just found out what the real risk was: not the model itself, but the absence of a governance layer underneath it.
|
By Harness
Your best engineer spent 500,000 tokens last week. Nothing shipped. There's a name for it now: tokenmaxxing. Failed prompts, dead PRs, code that never reaches production — it looks like productivity, but it isn't. Most engineering leaders can't tell you what percentage of AI-generated code actually ships, or where the budget went. You should be able to say "that bug cost me $2,700 in tokens to fix.".
|
By Harness
Bain says AI cost savings aren't arriving. But the value isn't missing, it's invisible. Most engineering teams can see token spend. They can see AI usage. What they can't see is whether any of it shipped, and whether it moved the needle on delivery. That's the measurement gap. And until it closes, AI ROI will keep looking worse than it should.
|
By Harness
Artifact governance should not depend on manual checks. But for many teams, container images, software packages, and open-source dependencies are imported into registries from multiple internal and external sources. Without automated guardrails, vulnerable images, untrusted packages, end-of-life dependencies, or non-compliant artifacts can reach developers and delivery pipelines.
|
By Harness
Managing MongoDB database changes shouldn't require manually creating and maintaining changelogs. In this video, you'll learn how Harness Database DevOps automatically generates MongoDB changelogs, helping teams capture existing database changes and bring them into version control for reliable CI/CD workflows. As a modern **database schema migration tool**, Harness Database DevOps helps teams automate database change management across relational and NoSQL databases, reducing manual effort and deployment risk.
|
By Harness
AI for Development Isn't New. AI for Delivery Is! AI coding assistants have transformed how teams create software. But innovation only delivers business value when code moves quickly and safely from commit to production and into customers' hands. In AI-Native Software Delivery, Harness Field CTO Nick Durkin and DevOps veterans Eric Minick and Chinmay Gaikwad present a practical guide to applying AI across the entire software delivery lifecycle.
|
By Harness
Organizations everywhere are racing to modernize DevOps and elevate the developer experience, but how close are they to actually delivering?We surveyed over 650 engineering leaders to find out. The result is The State of Software Engineering Excellence 2025, a report that uncovers the hidden challenges, gaps, and opportunities shaping today's software teams.
|
By Harness
This comprehensive whitepaper shows you how modern software delivery platforms solve these challenges.
|
By Harness
Modern systems are more complex-and more fragile-than ever before. Whether it's scaling challenges, dependency failures, or unpredictable outages, reliability is no longer optional. It's a competitive edge. This eBook provides a practical blueprint for successfully adopting Chaos Engineering, with strategies proven to work across engineering, SRE, and QA teams. Learn how to overcome internal blockers, align ownership, and embed resilience testing directly into your software delivery lifecycle.
|
By Harness
You're adopting AI code generation tools to enhance your engineering team's output, but how do you quantify the real return on investment? Without precise measurement, you're navigating in the dark, unable to identify true productivity gains or pinpoint areas for optimization. Justifying these critical AI investments becomes difficult.
- July 2026 (2)
- June 2026 (22)
- May 2026 (38)
- April 2026 (54)
- March 2026 (44)
- February 2026 (29)
- January 2026 (20)
- December 2025 (24)
- November 2025 (27)
- October 2025 (21)
- September 2025 (18)
- February 2025 (1)
Harness delivers intelligent AI automation, so your team ships code faster, safer, and smarter.
Don't let your pipeline become the bottleneck as developers and AI coding agents generate more code. Harness AI intelligently automates, safeguards, and accelerates software delivery at any scale.
- AI for DevOps & Automation: Unleash developer productivity with AI that understands your DevOps ecosystem. Harness combines the industry's fastest, most secure CI/CD with developer self-service to automate pipelines, infrastructure, and the entire path from code to production.
- AI for Testing & Resilience: Release software confidently using AI-powered predictive analytics and testing. Make every change fast, safe, and resilient, so your teams can focus on shipping quality code instead of chasing bugs and triaging outages.
- AI for Security & Compliance: Make secure software your new default. From application and API discovery to AI-powered threat prevention, Harness uses contextual insights and agentic workflows to detect and mitigate risks from build to post-deployment.
AI for Everything After Code.