Harness, a software development firm, has announced a significant update to its platform, bringing the power of generative AI to assist throughout the enterprise software development and delivery process. The company, founded in 2017, initially focused on automating the continuous integration/continuous delivery (CI/CD) process. With the new updates, Harness is enhancing its capabilities with a series of AI agents designed to accelerate the entire enterprise software development lifecycle.
The AI agents include AI DevOps Engineer (ADE), QA Assistant, AI Code Generation, and AI Productivity Insights service. Jyoti Bansal, CEO and co-founder of Harness, said, “Our primary thesis is that developers waste a lot of time doing all the toil. Toil includes all the various tasks that fall outside of coding.”
The AI DevOps Engineer automates complex tasks, such as creating pipelines for code building and deployment and attempting to fix failed deployments automatically.
The AI QA Assistant focuses on generating test automation, particularly for end-to-end testing of web and mobile applications. Bansal noted that this tool can reduce the time it takes to write tests by about 80%. Harness is also entering the AI code assistant market with its AI code assistant that uses Google Cloud’s Gemini models to provide real-time code suggestions and autocompletion capabilities.
The differentiation lies in its integration into the larger Harness platform. A new AI Productivity Insights tool aims to quantify productivity gains from using AI coding assistants by measuring metrics such as velocity, quality, and developer sentiment.
Harness unveils generative AI tools
The overarching goal of these new AI tools is to significantly boost developer productivity, with Bansal estimating that enterprise developer teams could become up to 50% more productive. Harness has also previewed three new modules for its DevOps toolset, featuring generative AI agents that automatically create DevOps pipelines and QA tests, alongside its software artifact registry with built-in supply chain security. These updates are expected to be generally available in early 2025.
The forthcoming modules include Cloud Development Environments, Artifact Registry, and Database DevOps. The Database DevOps module has attracted interest from customers such as Jignesh Patel, director of cloud and DevOps at Morningstar, who acknowledged the difficulty of rolling back database changes and appreciated the broader platform integration this module offers. Harness is also enhancing its tools with generative AI agents, with the new AI QA Assistant aiming to automate UI test generation and updating based on natural language requests.
Morningstar’s Patel remains open-minded but cautious about the new AI agents, noting that while Harness is catching up, it does not yet match the depth of established AI tools like GitHub Copilot. Chief Technology Officer Nick Durkin emphasized that these AI agents are designed to eliminate the most tedious aspects of software engineering. The decision to embed these AI tools within their workflow platform aims to reduce burnout among software development teams.
Durkin noted that, with the aid of AI, DevOps teams are expected to build and deploy more software in the next few years than they have in the past decade. While AI advancements have so far focused on individual productivity, embedded AI agents will significantly accelerate the application build and deployment processes. Despite some concerns about AI’s impact on the demand for software engineering expertise, Durkin believes AI will increase demand for skilled professionals capable of managing software development at an unprecedented scale.
