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Knowledge Graphs: The Backbone of AI-First Software Delivery | Harness Blog

--- ‍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.

Securing AI and Securing With AI: AI Security from Code to Runtime With Harness | Harness Blog

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.

5 AI And Cloud Cost Problems That Are Now Everyone's Problem

Not long ago, cloud cost was an engineering problem. FinOps teams owned it, finance leaned in occasionally, and everyone else stayed out of it. Now, that’s changed. AI changed who has skin in the game. CFOs get asked about it in board meetings. CEOs field questions on earnings calls. The audience for cloud cost management has exploded — and that means the conversation CloudZero is built to enable isn’t only a technical one, it’s a business one.

Incident Response Reimagined: Accelerating Resolution with AI Agents

Learn how PagerDuty is leveraging Agentic AI to transform the incident lifecycle from reactive firefighting to proactive prevention. Manuel Reis, Software Developer at PagerDuty, demonstrates how new tools like the SRE Agent and Scribe Agent assist engineers during high-pressure outages by autonomously triaging alerts, querying logs in tools like Grafana, and transcribing context directly into incident channels.

Prompt, Deploy, Pray Is Dead: Validating AI Code with Proxymock

Recent outages tied to AI-assisted code changes have pushed companies into a corner. After several incidents with massive “blast radius” impacts, organizations like Amazon introduced stricter controls—mandating that senior engineers manually review all AI-generated code before it hits production. That response makes sense on paper, but it exposes a fatal flaw in the modern development pipeline.

EV Fleets Don't Fail on the Road. They Fail in the Workflow. Agentic AI Fixes That.

You spent the last decade obsessing over connectivity. You bought into the hype that ‘data is the new oil.’ You fitted your entire fleet with sensors and built massive dashboards to track everything from battery cell temperature to tire pressure. The mission was simple: Capture every metric. Congratulations, you succeeded. You are now drowning in terabytes of data. But here is the hard truth: Data without action is just expensive noise.

Test your AI model training reliability, too

Training is at the heart of every LLM model, but it’s still an application running on an infrastructure, which means it can fail. Our GPU test helps you test your training GPUs so you don’t lose that valuable work. TRANSCRIPT: One of the things we built recently was the GPU Gremlin. So if you are training a bunch of models and you're doing a bunch of GPU testing. You know, we want to give you the tools to be able to go test that, to understand how training the model could fail.