Harness today made a DevOps platform infused with artificial intelligence (AI) capabilities that enable agents to automate a wide range of tasks and workflows generally available.

Trevor Stuart, senior vice president for Harness, said the Harness AI platform is based on a knowledge graph that has been created to enable DevOps teams to manage, govern and observe tasks assigned to AI agents. The overall goal is to provide a framework for incorporating AI agents into DevOps workflows that will eliminate much of the manual effort that is often encountered when setting up pipelines, he added.

At the core of the Harness AI platform is a Software Delivery Knowledge Graph that continuously updates data pulled from both Harness and third-party DevOps tools and platforms across every stage of the software development lifecycle (SDLC). That knowledge graph enables AI agents to generate pipelines, rollback deployments and automatically run root-cause analysis.

For example, via a natural language, it is now possible for DevOps teams to simply describe their intent for setting up a pipeline, which Harness AI will build and deploy in adherence to the policies and guidelines an organization has defined, said Stuart.

Harness AI can also create, update, and maintain tests and chaos experiments, detect vulnerabilities and surface actionable insights to help reduce cloud costs, he added. None of the data collected by Harness AI, however, will be used to train any of the underlying AI models that Harness agents are invoking.

Harness claims existing Harness customers who participated in the Harness AI beta program have already seen downtime being cut in half, along with a 50% reduction in the amount of time spent debugging pipelines. Additionally, Harness reports test cycle times reduced by 80% and test maintenance efforts cut by 70%.

While DevOps teams are clearly embracing AI coding tools, the software engineering workflows used to deploy software don’t easily scale because of a dependency on overly brittle scripts. Harness is making a case for an AI platform that assigns tasks to AI agents that have been specifically trained to perform them. That approach makes it possible to not only build and deploy applications at greater scale, it also reduces the number of pipelines that need to be maintained by better optimizing workflows, said Stuart.

At this juncture, it’s not a question of whether DevOps teams will be relying on AI agents to automate tasks so much as it is when and to what degree. The one certain thing is that DevOps engineers will be needed to not only review the output created by those AI agents, but also orchestrate the management of workflows that might soon include hundreds of AI agents.

Hopefully, thanks to the rise of AI agents, much of the tedious manual effort that often conspires to burn out DevOps engineers will soon be reduced, if not entirely eliminated. The challenge and the opportunity now is building, deploying and maintaining the platform used to orchestrate the management of a portfolio of AI agents that, much like humans, will all seem to have their own set of unique personality attributes.