Operations | Monitoring | ITSM | DevOps | Cloud

In the fast-paced world of modern software delivery, compliance is often a bottleneck. While our existing OPA-based Policy as Code feature has long empowered teams to encode complex authorization checks and enforce granular governance across their DevOps workflows, we know that starting from a blank page can be daunting. Security and governance teams struggle to keep up with the volume of releases, while developers often find the initial setup of these policies to be time-consuming.

The open-source landscape has witnessed another highly automated, ecosystem-level subversion. On June 17, 2026, a critical software supply chain attack struck the Mastra AI framework - a popular open-source TypeScript ecosystem used widely to build AI agents, workflows and RAG pipelines. By exploiting a compromised contributor account, threat actors successfully mass-published 144 malicious packages under the official @mastra npm scope.

When we launched Autonomous Worker Agents, the message we led with was simple: governance is inherited, not integrated. Agents don't get security bolted on after the fact. They inherit the OPA policies, RBAC, and audit trails already running your production pipelines. This post is about the layer underneath that promise: isolation. We let an Autonomous Worker Agent run shell commands and call APIs inside our pipelines.

We shipped 62 features in June: roughly one every twelve hours! That pace isn't an accident. It's what happens when AI is writing more of the code, generating more of the tests, and clearing more of the review queue, and the rest of the delivery pipeline has to keep up.

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.

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.