AI-powered software development acceleration: Harness, a software development firm, has unveiled a significant platform update that integrates generative AI throughout the enterprise software development and delivery process.
- The update aims to streamline and automate various aspects of the software development lifecycle, moving beyond just code generation to address the entire process.
- Harness, founded in 2017, has evolved from focusing on CI/CD automation to becoming a comprehensive software delivery platform.
Key AI agents introduced: The platform update includes several AI agents designed to enhance different stages of the software development process.
- AI DevOps Engineer (ADE): An advanced agent that can perform complex tasks such as creating pipelines for code building and deployment, and even attempting to fix failed deployments automatically.
- QA Assistant: Focuses on generating test automation, particularly for end-to-end testing of web and mobile applications, reportedly reducing test writing effort by about 80%.
- AI Code Generation: A new addition to the Harness platform, using Google Cloud’s Gemini models to provide real-time code suggestions and autocompletion capabilities.
- AI Productivity Insights: A tool that measures and compares developer productivity, providing quantifiable data on the effectiveness of AI coding assistants.
Addressing developer ‘toil’: Harness CEO Jyoti Bansal emphasizes the company’s focus on reducing time-consuming tasks outside of coding.
- The AI agents are designed to automate and streamline various aspects of the development process, from coding to deployment and testing.
- Bansal suggests that this approach could make enterprise developer teams up to 50% more productive.
Competitive landscape: The introduction of AI code generation places Harness in competition with established players in the AI coding assistant market.
- Competitors include GitHub Copilot, Replit, Tabnine, Oracle, and AWS, among others.
- Harness differentiates itself by integrating the AI code assistant into its broader platform, offering a more comprehensive solution.
Productivity measurement: The AI Productivity Insights tool addresses the challenge of quantifying the impact of AI coding assistants on developer productivity.
- The tool measures metrics such as velocity (including code commits and lines of code), quality, and developer sentiment.
- This approach aims to provide concrete data on productivity gains, moving beyond anecdotal claims.
Broader implications: Harness’s AI-driven approach to software development represents a significant shift in how enterprise software may be created and deployed in the future.
- By automating various aspects of the development lifecycle, this technology could potentially allow developers to focus more on creative problem-solving and innovation.
- However, the long-term impact on job roles and skill requirements in software development remains to be seen, as AI takes on more traditionally human tasks in the process.
Harness aims to accelerate enterprise software development with AI agents
