Harness today expanded its continuous delivery (CD) platform to add capabilities such as release orchestration and automated rollback that make it simpler for software engineers to manage code at scale.
Bradley Rydzewski, senior vice president at Harness, said these additional capabilities are now core requirements for DevOps teams that are managing much higher volumes of code as more applications are being built and deployed in the artificial intelligence (AI) era.
In addition to Release Orchestration and AI-Powered Verification and Rollback capabilities, Harness has added Snowflake support to the Database DevOps feature of its CD platform, along with a Warehouse-Native Feature Management and Experimentation capability to its CD platform that makes it simpler to manage different branches of a code base.
The issue that Harness is trying to address is that there are, on average, 33 processes that software engineers need to manage after code is checked in, said Rydzewski. As more code is created, managing code bases becomes that much more challenging, he added.
Collectively, the capabilities Harness is adding to its CD platform will enable DevOps teams to rise to that challenge, said Rydzewski.
For example, the AI-Powered Verification and Rollback capabilities automatically connect to existing observability tools and platforms. It then identifies which signals matter for each release, and determines in real time whether a rollout should proceed, pause, or be rolled back. The overall goal is to make rollbacks routine versus an event requiring a lot of manual effort, said Rydzewski.
The Release Orchestration capability, meanwhile, makes it simpler to coordinate deployments across multiple teams of software engineers without relying on spreadsheets.
While the scope of the challenges involving AI-generated code will vary from one team to the next, many long-standing bottlenecks and other related issues are now being exacerbated. A recent Harness survey finds that thanks to increased reliance on artificial intelligence (AI) coding tools, well over a third (35%) are either achieving daily or more frequent product deployments, with 36% deploying software multiple times per week. However, more than half (51%) also noted AI-generated code leads to deployment problems at least half the time.
More than three-quarters (78%) admit they have fragmented delivery toolchains, with 70% of respondents also conceding their pipelines are plagued by flaky tests and deployment failures.
Additionally, more than three-quarters (77%) said teams often need to wait on others for routine delivery work before they can ship code and only 21% said they can add functioning build and deploy pipelines to an environment in under two hours.
It’s not clear to what degree the rise of AI coding will require DevOps teams to revisit the tools and platforms they currently rely on to manage codebases. However, it’s becoming increasingly apparent that many of them were designed for a different era of software development. In some cases, the rise of AI coding will spur further adoption of best platform engineering practices to manage DevOps workflows at higher levels of scale.
Of course, as every DevOps team knows, recognizing there are problems that need to be addressed and marshaling the necessary resources to address them are not always one and the same thing.