Harness today unfurled a bevy of additions to its portfolio that include artificial intelligence (AI) agents that are deeply embedded into DevOps workflows.

Announced at its annual {unscripted} 2024 event, the latest additions to the Harness platform augment DevOps teams with AI agents that are trained to automate specific tasks.

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Harness CTO Nick Durkin said each of these AI agents is specifically designed to eliminate manual tasks that most software engineers would rather not have to manually perform themselves. AI, in general, should be used to eliminate the worst parts of being a software engineer, he noted.

Collectively, there are three Harness AI Assistants, including one that automates routine pipeline generation and automatically triages and repairs workflow issues. The other two agents enable DevOps teams to generate code and build self-healing test suites, respectively.

Rather than having to separately license each of these AI agents, Harness is opting to embed them within a DevOps workflow as part of an effort to reduce the overall level of burnout any software development team might experience, said Durkin.

Additionally, Harness is also making available Harness AI Productivity Insights, a tool that enables organizations to measure the effectiveness of AI code generated and the impact it is having on software development.

Harness is also making available a beta release of its own artifact registry to reduce friction across DevOps workflows, while also extending the reach of its DevOps platform to now also include deployment of changes to databases.

The company has also updated a DevSecOps module to enable software engineering teams to continuously manage governance, risk management, and compliance across the entire DevOps toolchain.

Finally, Harness is now making available pre-configured cloud environments that DevOps teams can use to write and debug code without having to provision their own environment and making available Harness Open Source, a software delivery platform that enables DevOps teams to code, build, manage artifacts, and deploy software from a single, centralized environment.

It’s expected that DevOps teams with help from AI should be able to build and deploy more software in the next few years than they might have deployed in the past decade. While most of the AI advances made thus far have tended to focus on making individual developers more productive, AI agents embedded in DevOps workflows will play a critical role in enabling organizations to build and deploy applications faster, noted Durkin.

It’s still relatively early days so far as usage of AI within DevOps workflows is concerned, there is little doubt that software engineers will be looking to leverage these capabilities to reduce toil, said Durkin. In many cases, software engineers are not going to want to work for organizations that don’t provide them with the AI tools that make them more productive, he added.

Of course, there may be some natural concern about the impact those AI tools and agents will have on the demand for software engineering expertise. However, in the final analysis, it’s likely that rather than replacing software engineers AI will instead create greater demand for anyone who has the expertise and experience required to manage software development and deployment at a level of scale that not too long ago might have seemed unimaginable.