Harness AI is built on a continuously updating Software Delivery Knowledge Graph that aggregates data from Harness and third-party DevOps tools across the entire SDLC. AI agents leverage that graph to generate pipelines, rollback deployments and run automated root-cause analysis. Natural language inputs allow teams to describe pipeline intents, which the platform builds and deploys following organizational policies. The platform can create, update and maintain tests and chaos experiments, detect vulnerabilities, and surface cost-reduction insights. Collected data will not be used to train the underlying AI models. Beta customers reported downtime halved, debugging time reduced by 50%, test cycle times cut 80%, and test maintenance lowered 70%.
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,
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.
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