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By Nicole Morgan
Here's the uncomfortable truth about the Mythos era: knowing about a vulnerability and being able to neutralize it are two entirely different problems. AI models like Mythos are finding vulnerabilities 10x faster than humans ever could. Project Glasswing participants discovered over 10,000 high and critical vulnerabilities in their applications. Firefox alone had 271 previously unknown zero-days exposed by Mythos. That's the good news.
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By Moshe Tsur
At Harness, we build an AI-powered software delivery platform, and test result data is core to how we help engineering teams ship faster. The table that stores it started small: one row per record, all the context right there on the row. Simple, readable, and it worked. Until it didn't. This is the story of how we refactored it, what we learned, and what I'd tell you to watch for in your own systems.
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By Pritesh Kiri
The latest Resilience Testing documentation update brings Chaos Hub directly into the docs, making it easier to discover and use fault, probe, and action templates. It also introduces a Prompt Library with ready-to-use AI prompts for Harness MCP, helping teams run resilience workflows faster using natural language.
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By Nicole Morgan
AI has fundamentally changed software development. Developers are writing more code than ever. AI coding assistants can generate features, tests, documentation, and infrastructure configurations in minutes. Engineering organizations are seeing meaningful productivity gains as AI becomes embedded throughout the software development lifecycle. But there is a catch. Security teams now face a difficult reality: application security was already struggling to keep pace with software delivery before AI arrived.
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By Kelsey Rosen
Cloud cost visibility at scale usually works great… until it suddenly doesn’t. At first, everything feels manageable. You can track spend by service. You know which team owns which resources. Reports are clean, and the numbers make sense. Then one day, there’s a $47,000 spike spread across three AWS accounts that no one noticed for eleven days. Leadership wants answers. Engineering wants context. And your carefully designed tagging strategy?
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By Shibam Dhar
AI is changing artifact management in two ways at once. Every AI-generated pull request, dependency update, and automated build creates more container images, packages, and Helm charts than ever before. Registries are growing faster than engineering teams can manage them, driving up storage costs and leaving thousands of stale artifacts behind. At the same time, the cost of deleting the wrong artifact has never been higher.
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By Austin Lai
As organizations ship software faster than ever, runtime behavior changes are becoming just as frequent as code releases. Teams need a way to update application behavior without waiting for code deployments while maintaining visibility, governance, and control. Now available in beta, Config Management provides a governed runtime control plane that separates runtime configuration from application deployments, enabling organizations to deliver configuration changes instantly across environments.
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By Chinmay Gaikwad
Harness shipped 71 features in July, about one every 10 hours. That's more than June's 62, and the surge lines up with what AI is doing to the rest of the SDLC: coding agents are writing more of the code, test agents are now generating and running more of the tests by default, and every stage downstream: deployment, security, cost, and resilience has to absorb that pace without falling over.
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By Patrick Brogan
AI has quickly become one of the largest and fastest-growing enterprise expenses, exposing many of the same governance and visibility challenges organizations previously faced with cloud. Based on findings from the 2026 State of AI in FinOps report, we explore how mature organizations improve AI cost ownership, reduce waste, and build a culture focused on measurable business value.
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By Animesh Pathak
Versioning reference data in Git with Liquibase OSS changelogs enables consistent, auditable, and automated deployments across environments. Using loadUpdateData with versioned CSV files provides fast, reliable rollbacks and reduces production risk. Modern applications do not just depend on schema changes. They also depend on data that powers the application itself.
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By Harness
What happens when AI agents can operate faster than humans can monitor them? In this clip, we discuss the risks of autonomous AI systems, why human oversight may not be enough, and the growing need for machines that can monitor other machines.
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By Harness
Claude Code changes how fast software gets written. Harness changes whether you can trust what shipped. In this video, watch how autonomous AI agents handle end-to-end bug fixing, security remediation, and deployment verification—all within an automated Harness pipeline. From reading a ticket to running canary deployments and self-healing broken manifests, see how engineering teams can deliver software faster without sacrificing quality or security.
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By Harness
For every $1 you pay OpenAI or Anthropic, it's costing them about $1.60. AI is running at a loss — so is the whole business model broken? The full bill for AI hasn't landed yet. In this ShipTalk short, Field CTOs Adam and Martin break down the economics of generative AI: why the frontier labs lose money on every prompt, why they'd need to raise prices ~60% just to break even, and the bet the entire industry is making — that inference costs drop fast enough to catch up. Plus the Gartner forecast every CFO should see: by 2028, the AI bill could be bigger than the employment bill.
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By Harness
AI agents are now hacking on their own — and it already happened to two of the world's biggest AI labs. OpenAI's models broke out of a test sandbox, exploited a vulnerability, and hit Hugging Face's production systems. Days later, Anthropic reviewed over 141,000 evaluation runs and found three of its own Claude models had done the exact same thing to three different organizations.
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By Harness
OpenAI just disclosed that two of its own AI models went rogue during an internal red-team test — escaping their sandbox, reaching the open internet, and hacking Hugging Face on their own. OpenAI called it an“unprecedented cyber incident.” So are autonomous coding agents already out of control? In this episode of ShipTalk — brought to you by Harness — hosts Martin Reynolds and Adam Arellano break down the story that reads like science fiction, then get to the harder truth underneath it. In the same week, OpenAI, Anthropic, and Google all shipped repository-wide coding agents within 24 hours of each other.
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By Harness
As AI software engineers like Cognition's Devin accelerate code production, downstream delivery, and governance processes must keep pace. In this video, see how Harness closes the gap by providing autonomous oversight for autonomous code. Watch a step-by-step demonstration of Devin fixing a real defect in a broken banking application while the Harness platform stands between the fix and production to ensure complete safety and validation.
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By Harness
AI agent quality should not depend on manual checks. But for many teams shipping AI in production, agent failures are silent. The agent doesn't crash - it just gives confidently wrong answers, and your monitoring sees nothing wrong. Without automated guardrails, plausible-sounding wrong responses, hallucinations, and quality regressions reach customers before anyone notices.
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By Harness
In this video, we explore two major improvements to the Harness Resilience Testing documentation designed to help you build and manage chaos experiments more efficiently. What's new: ChaosHub Integration AI Prompt Library for Harness MCP These updates make it significantly easier to discover chaos experiments and leverage AI throughout your chaos engineering workflow. If you're using Harness Resilience Testing, this walkthrough will help you get started quickly and make the most of the new documentation experience.
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By Harness
Harness Agent DLC: Ship AI Agents to Production Safely Building an AI agent is easy. Getting one into production safely is where teams get stuck. Harness Agent DLC extends the software delivery lifecycle to AI agents, giving teams a clear path to evaluate, deploy, secure, observe, and optimize agents in production. Learn more: Because agents dynamically choose their own tools, APIs, and actions, their behavior can change every time they run. Harness Agent DLC gives engineering teams the controls needed to move beyond experiments and operate agents safely at scale.
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By Harness
Microsoft patched 570 security vulnerabilities this Patch Tuesday — but every patch is also a public disclosure. The second those fixes drop, attackers know exactly where 570 weaknesses live. The only question that matters: can you find and patch them across your entire environment before someone exploits them? The speed of discovery is only increasing. Are you ready for the velocity of this new world? Let us know in the comments.
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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.
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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.
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By Harness
This comprehensive whitepaper shows you how modern software delivery platforms solve these challenges.
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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.
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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.
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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.