June 2026 Summaries

Harness has introduced Autonomous Worker Agents designed to streamline the software delivery process by automating tasks between code writing and production deployment. These agents function as pipeline steps and are powered by the Harness MCP, integrating seamlessly into existing software delivery pipelines with full governance, including scoped credentials, OPA policy enforcement, and audit trails. They are designed to operate safely in production environments, with mechanisms such as sandboxing, scoped credentials, and audit trails ensuring security and compliance. The agents can be created using YAML, a UI, or through integration with existing coding agents, and they are capable of performing a wide range of tasks, including code review, security remediation, and feature flag management. Harness also offers a marketplace for teams to discover, customize, and manage these agents, with pre-built solutions available for common use cases. The platform supports a variety of models and allows for dynamic model selection, providing flexibility and cost management for enterprises.

Harness, a company focused on modernizing federal software delivery, has launched the Canary Partner Program to support federal agencies in implementing an automated early warning system akin to a miner's canary. The program is designed to help federal entities like the Department of Defense, Civilian Agencies, and the Intelligence Community prioritize governance, security, and risk management as they accelerate IT modernization. The initiative aligns with current federal mandates such as AI and cloud-first directives, zero trust requirements, and continuous ATO expectations. The program offers flexible engagement models, including co-sell, resell, or managed services, and provides partners with resources like a self-service portal, compliance resources, and co-marketing support. As demand for such solutions grows, the Canary Partner Program aims to scale Harness's capabilities across the federal ecosystem by partnering with government-focused resellers, systems integrators, and consulting firms.

Harness MCP Server's integration with Google Antigravity enables developers to connect their software delivery systems directly within the editor, allowing real-time access to pipelines, execution history, and more, without leaving the editor environment. This integration bridges the gap between rapid code development and slower delivery processes by leveraging the Harness Software Delivery Knowledge Graph, which provides structured context for accurate and fast agent actions. The setup is straightforward, requiring only a Personal Access Token and a few configuration steps. Once connected, the integration facilitates various software delivery tasks such as creating pipelines, inspecting executions, and debugging deployments through plain language interaction, all while maintaining robust security and compliance through RBAC enforcement and audit logging. This setup allows developers to manage their delivery system comprehensively within Antigravity, ensuring that AI operates with the same understanding and context as it does for the codebase.

AI coding assistants, while accelerating development, can inadvertently introduce vulnerable, malicious, or non-compliant open-source dependencies into codebases. This is particularly risky as these tools suggest packages based on popularity rather than security, leading to potential supply chain vulnerabilities, as evidenced by incidents like the TanStack supply chain attack. Traditional security measures often detect vulnerabilities too late, whereas Harness Artifact Registry's Dependency Firewall provides a proactive solution by evaluating and blocking risky packages at the registry level before they enter CI/CD pipelines. This firewall approach ensures that only secure and compliant packages are integrated, maintaining development speed without compromising security. By establishing a control point at the registry boundary, organizations can prevent unsafe packages from entering their ecosystems, thereby balancing the speed-security tension inherent in AI-assisted development workflows.

Harness has been recognized as a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms for the third year in a row, highlighting its position as the furthest on the Completeness of Vision axis. The company attributes this accolade to its continued investment in AI-powered DevSecOps, which enables engineering, security, and operations teams to manage software delivery across the entire software development lifecycle (SDLC) with enhanced security, reliability, and governance. Harness serves a diverse clientele, including major corporations like United Airlines and Citi, by reducing delivery complexity and boosting developer productivity through a platform that integrates AI, security, and cost management solutions. As the software delivery landscape evolves with the rise of AI coding assistants, Harness emphasizes the importance of platforms that ensure secure, reliable business outcomes amidst accelerated code generation. The company continues to innovate through product development, open-source contributions, and strategic acquisitions, with a focus on creating a unified platform that addresses security, risk management, and the growing influence of AI in software development.

Harness CI Cloud's CPU and Memory Insights feature addresses the challenges of resource visibility in cloud-based CI pipelines by providing real-time and historical data on CPU, memory, and disk I/O usage without impacting build performance. This tool helps teams optimize resource allocation by identifying whether builds are CPU, memory, or I/O-bound, allowing for right-sizing of virtual machines to reduce costs. It also aids in debugging by pinpointing resource spikes related to specific build steps and detecting regressions due to dependency updates. The system uses a lite-engine component within the ephemeral VM to collect and stream resource metrics, which are visualized through interactive charts in the UI, offering full coverage regardless of build step outcomes. The feature is compatible across various platforms, including Linux, macOS, and Windows, and operates with minimal overhead, ensuring reliability and efficiency for continuous integration processes.

Platform Engineering teams aim to build secure, scalable internal developer platforms to accelerate time-to-market, but mainframe applications often remain isolated, causing operational challenges. These legacy systems, often updated at a slower pace, are integral to modern enterprise software, where a single feature might involve various technology stacks. To address this, integrating mainframe applications into a unified developer platform alongside cloud-native services can mitigate risks and reduce technical debt. By using modern CI/CD tools, such as IBM DBB and Wazi Deploy, teams can automate and secure mainframe deployments, enabling a seamless orchestration across both distributed and mainframe pipelines. Harness's platform offers a comprehensive solution by providing Enterprise Release Orchestration, which coordinates multi-service releases and enhances visibility, security, and efficiency, ultimately reducing total cost of ownership and improving system reliability.

Azure deployment has evolved beyond basic pipelines to incorporate advanced strategies such as CI/CD automation, progressive delivery, and feature flags, which together enable faster, safer shipping with fewer incidents. Choosing the right deployment strategy—whether canary, blue-green, or rolling deployments—can help reduce the blast radius and ensure quick rollbacks. A mature deployment workflow integrates feature management and experimentation, separating code deployment from release, thus allowing for controlled feature exposure through feature flags. Infrastructure as Code (IaC) ensures consistent environments and prevents configuration drift by defining all resource changes in code. Throughout the deployment process, observability, post-deployment verification, and automated rollback mechanisms ensure that any issues are promptly addressed. Harness enhances Azure deployment with its comprehensive suite of tools for CI/CD, feature management, infrastructure automation, and observability, providing enterprise-grade governance and policy enforcement directly within the deployment pipeline.

The Harness VS Code Extension, now available on the VS Code Marketplace, integrates various CI/CD functionalities directly within the VS Code editor to streamline software delivery workflows. This extension enables real-time pipeline monitoring, log viewing, and deployment approvals without leaving the coding environment, thereby reducing context switching and improving efficiency. It utilizes AI-assisted debugging tools like Claude Code, GitHub Copilot, and Cursor, which integrate seamlessly, providing structured and context-aware data for resolving pipeline issues effectively. The extension leverages the Harness Software Delivery Knowledge Graph to provide comprehensive execution context, enhancing the AI's accuracy in troubleshooting. Installation is straightforward, requiring a search and installation from the Extensions view, and supports OAuth authentication for effortless setup. This tool aims to centralize software delivery processes within VS Code, significantly simplifying and accelerating the workflow for developers.

Cloud infrastructure has evolved beyond simple provisioning, and the challenge now lies in managing operations at scale due to infrastructure drift and fragmented workflows. Traditional Infrastructure as Code (IaC) pipelines, while effective for initial provisioning, fall short in dynamic environments where continuous governance is crucial. Platform engineering teams are increasingly adopting infrastructure control planes that provide continuous observation, policy enforcement, and drift detection to unify provisioning, configuration, and operational workflows. This shift towards embedding governance directly into developer workflows enhances operational consistency and security, allowing for proactive remediation and scalable self-service infrastructure. Solutions like Harness facilitate this by integrating multiple functions into a unified system, thus improving developer autonomy while maintaining centralized control.

Harness introduces the capability to evaluate Open Policy Agent (OPA) policies on local infrastructure, specifically on Kubernetes clusters, to enhance security and compliance for enterprises with strict data residency and firewall rules. This approach allows policy evaluations to occur within the corporate trust boundary, safeguarding sensitive credentials and data without sacrificing the speed and efficiency of continuous delivery pipelines. By enabling local OPA evaluation, Harness addresses challenges such as accessing secure internal systems, managing large or sensitive payloads, and maintaining pipeline integrity without exposing critical information to external environments. This development ensures that enterprises can adhere to compliance requirements while maintaining developer productivity and toolchain efficiency.

In the rapidly evolving landscape of software engineering, the traditional model of human code review is increasingly challenged by the accelerated pace of AI-generated code. This shift necessitates a more empirical approach to software deployment, where feature flags and trunk-based development help decouple deployment from release, allowing code to be continuously integrated and gated until deemed safe for user exposure. Joshua Klein discusses how the separation of deployment from release, facilitated by feature flags, allows for controlled exposure and feedback in production environments, which remain the ultimate testbed for evaluating the real-world impact of software changes. The article emphasizes the importance of using metrics to assess the impact of features during rollout, thus transforming the release process into an iterative cycle of deployment, observation, and adjustment, where production metrics become critical in determining the success of a feature. The focus shifts from predicting all possible outcomes during code review to measuring actual outcomes in production, highlighting the necessity of alert systems and automated controls for managing risks associated with progressive delivery in the AI era.

In June 2026, a significant supply chain compromise was identified in the @redhat-cloud-services npm namespace, involving 32 packages and 96 versions republished with a credential-stealing worm known as Miasma. This attack, a descendant of the Mini Shai-Hulud worm, exploited a compromised Red Hat employee's GitHub account to inject malicious code directly into Red Hat's internal repositories, bypassing code reviews. Miasma's novel approach included generating uniquely encrypted payloads for each infection, targeting cloud identities beyond static keys, and utilizing short-lived OIDC tokens for trusted publishing. The malware propagated itself by infecting software packages through stolen credentials, making it difficult to detect and prevent. The incident underscores the vulnerability of automated trust systems in open-source ecosystems, highlighting the need for real-time visibility into dependencies and robust measures to block compromised versions. Harness Supply Chain Security (SCS) offers tools to help detect and contain such threats by providing end-to-end visibility, enabling teams to quickly identify and mitigate the impact of compromised packages across their supply chain.

Harness's recent updates emphasize enhancing software development efficiency through AI coding tools, cloud cost management, and robust security measures. The May release introduced over 60 features, including cloud and AI cost management capabilities that track AI infrastructure as a primary spend category and correlate AI assistant adoption with productivity outcomes. The inclusion of the Harness MCP Server in the Claude Connectors Directory allows developers to access pipelines and workflows directly within the Claude interface, leveraging the Harness Software Delivery Knowledge Graph for informed decision-making. Security enhancements include keyless artifact signing and sensitive data detection within AI prompts, while the Dependency Firewall now supports exemptions and notifications for more precise control. Additionally, Harness has improved its testing and resilience processes by integrating Kubernetes load testing and expanding AI test automation with native Playwright execution. A focus on AI spend visibility, engineering metrics refinement, and platform integrations such as Google Chat and ServiceNow aims to streamline operations and provide comprehensive insights into AI and cloud expenditure.

Harness has announced several strategic partnerships and integrations to enhance its software delivery platform, including the inclusion of Harness in the Claude Connectors Directory for real-time AI access to software delivery pipelines. They have launched Harness CI/CD for AWS, which is now available in the AWS Marketplace to help developers across AWS applications. Additionally, Harness has partnered with Semgrep to integrate open source SAST scanners into Harness STO and become the first certified CI/CD platform vendor to partner with Wiz, integrating the Wiz CLI into their STO. Furthermore, Harness has introduced Cloud Cost Management within Datadog to provide better observability and monitoring for cloud costs. These developments aim to empower developers and businesses by offering a more efficient, integrated, and cost-effective software delivery experience.

Harness MCP Server has been integrated into the official Claude Connectors Directory, allowing developers using Claude to seamlessly access Harness's extensive software delivery capabilities, including pipeline management, deployment oversight, and security checks, by leveraging the Harness Software Delivery Knowledge Graph. This integration offers a significant improvement over traditional API access by providing a structured, semantically annotated model that allows AI agents to interact with and understand the delivery system with precision and reliability. Harness's Knowledge Graph enables the AI to execute complex queries across multiple domains without resorting to guesswork, reducing the risk of errors and ensuring that actions comply with existing governance and permission structures. The inclusion in the Claude Connectors Directory enhances discoverability and trust, offering a straightforward OAuth connection process and aligning with broader efforts to integrate Harness into various AI ecosystems.