June 2025 Summaries

Harness has launched AI Test Automation, an innovative AI-native solution designed to streamline the software testing process by integrating seamlessly into the CI/CD pipeline, eliminating manual testing bottlenecks, and enhancing the speed and quality of software delivery. This groundbreaking platform allows for rapid, no-code test creation and adaptive, self-healing test maintenance, significantly reducing the time and resources traditionally spent on quality assurance. With features such as intent-based testing, AI-generated selectors, and intelligent retries, it offers a smarter, faster, and more resilient approach to testing across the software development lifecycle. The solution has already demonstrated substantial success, with companies like Siemens Healthineers and Wasimil reporting significant reductions in test creation time and maintenance efforts, leading to accelerated release cycles and improved developer experiences. As the first fully automated software delivery platform, Harness AI Test Automation marks a pivotal shift in DevOps practices, offering enterprise-grade security and the flexibility to incorporate custom scripts for complex scenarios, thereby empowering organizations to focus on delivering new features rather than fixing bugs.

Harness Open Source v3.2.0 introduces several enhancements aimed at improving Git workflows, particularly in CI/CD pipelines, by offering features such as Git LFS support, one-click pull request reverts, and dynamic reviewer assignments. The update allows for seamless handling of large binaries with Git LFS, provides an easy way to revert problematic pull requests, and supports changing the target branch of a pull request without closing it. It also enhances code review processes by automatically assigning Code Owners as reviewers and enabling default reviewers per branch, which is crucial for teams with specific review requirements. Additionally, a new commit authentication setting increases security by ensuring commits match the authenticated user. These improvements focus on enhancing the developer experience and automating workflows for teams using Harness Open Source as a core component of their CI/CD and artifact management systems.

Engineers can utilize controlled experimentation with feature flags to test and optimize infrastructure decisions, ensuring robust and performant applications without affecting users. Infrastructure experimentation extends beyond typical product management roles, covering foundational decisions like database selections and caching strategies that impact performance, scalability, and reliability. By experimenting directly in production using feature flags, teams can gather real-world data, reduce uncertainties, and safely validate critical infrastructure changes. This approach allows for gradual rollouts, safe rollbacks, and fosters a culture of continuous experimentation, helping engineers confidently make data-driven decisions and improve system reliability. Feature flags enable controlled rollouts and quick adjustments, mitigating risks associated with infrastructure changes, while tools like Harness Feature Management & Experimentation provide comprehensive support for these processes.

Animesh Pathak's article narrates his transition from managing manual and error-prone SQL migrations to adopting an automated, version-controlled approach using Harness Database DevOps. Initially, Pathak encountered numerous challenges such as fragile SQL script execution, difficulty in setting up test environments, downtime risks, and lack of visibility leading to database drift. These issues prompted the need for automation to improve deployment speed, reliability, and traceability. By integrating Harness Database DevOps, Pathak was able to streamline schema management through a GitOps-style workflow, employing tools like Liquibase for automatic migration and rollback processes, significantly reducing deployment time and errors. The article emphasizes the importance of starting small with structured ChangeLogs and leveraging automation to enhance the efficiency and confidence of database deployments, ultimately transforming the software development lifecycle by eliminating bottlenecks and ensuring consistent, transparent processes across environments.

Harness has released a major update to its Internal Developer Portal (IDP), enhancing the Backstage framework to better meet the needs of large enterprises with complex requirements. This update introduces features like real-time Git synchronization, an advanced role-based access control system, and a customizable catalog user experience, designed to streamline the developer experience by providing structure, speed, and control. The IDP now supports organizing entities according to the company's hierarchy, allowing teams to manage services relevant to their specific areas, thereby improving efficiency and reducing errors. The release also includes a UI-driven entity management system that simplifies onboarding and service registration, making it accessible even to those without configuration syntax knowledge, while maintaining support for traditional YAML workflows. Additionally, new APIs facilitate large-scale adoption through automation, supporting use cases such as auto-discovery and CLI integration, and the platform remains compatible with existing Backstage plugins, ensuring continuity for users familiar with the ecosystem.

Continuous deployment is a crucial aspect of DevOps that aims to automate the release of software to increase speed, reduce risk, and alleviate developer toil. It involves a seamless deployment workflow where code can go live almost immediately after passing automated tests, unlike continuous integration and continuous delivery, which involve more manual steps. Effective continuous deployment requires a combination of automation and developer-friendly guardrails, focusing on tools that integrate well with existing systems, offer scalability through pipeline templates, and provide user-friendly governance. Misconceptions, such as the need for a fully automated pipeline from the start, can hinder progress, but incremental automation reduces toil and risk. Companies like Ancestry have seen significant efficiency improvements by implementing reusable features, and modern tools allow developers more control over their pipelines. Harness is highlighted as a practical example of a continuous deployment tool that provides a script-free deployment experience and manages automation at scale, with features like AI-powered rollbacks and environment-aware role-based access control, thus offering a cohesive platform for enhanced software delivery.

Harness has been recognized as a Leader in The Forrester Wave™: DevOps Platforms, Q2 2025, for its innovative AI-driven approach that enhances DevOps capabilities by reducing engineering toil and easing cognitive load across the software development lifecycle. The company achieved top scores in AI infusion, innovation, and vision, with its platform offering robust deployment automation and superior data analytics capabilities that empower teams to make informed, data-driven decisions. Harness's customer-centric approach ensures strong partnerships, allowing even smaller organizations to influence product development. This recognition underscores Harness's commitment to continuously improving its platform and supporting customers in their DevOps transformation journey, while Forrester maintains its independence in evaluations.

APM Probes are tools designed to enhance chaos engineering by providing objective measurements of application performance during failure experiments, utilizing existing monitoring systems such as Prometheus, AppDynamics, and Splunk. These probes help establish a baseline for normal application behavior and enable real-time monitoring to assess resilience when introducing variables like network partitions or resource exhaustion. By offering clear pass/fail results based on actual performance data, APM Probes eliminate guesswork, allowing teams to confidently evaluate their systems' ability to handle unexpected issues. Specifically useful in Kubernetes environments, they integrate seamlessly with existing observability infrastructure, making chaos engineering more accessible without the need for additional instrumentation.