Even as organizations rely on AI to create code and engineering teams are releasing daily or even more often, those same teams report that almost a quarter of their deployments require remediation, and their time to remediate is up to more than 7 ½ hours.
This, according to the 2026 State of DevOps Modernization Report from Harness, clearly reflects that the release process is what’s causing this problem.
To address that gap, Harness today is unveiling new Release Orchestration capabilities with AI-enabled verification and rollback that the company said automatically decides whether each release should proceed. Also now native to the company’s pipeline are warehouse-native feature management and experimentation (from its 2024 acquisition of Split Software) and Database DevOps for Snowflake, so changes to code and data move through the same delivery flow, the company said.
“What we’re seeing on the ground, is the amount of code that developers and tools are creating, and the rest of the process simply can’t keep up,” Bradley Rydzweski, senior vice president at Harness, told SD Times. “So you have this massive bottleneck where you have this incredible coding velocity, and it just hits a brick wall because there just aren’t enough people to review it and to release it. Basically, everything after the code.”
The crux of today’s Harness releases, he said, is “just helping alleviate those pain points.”
Among the release orchestration features is the ability to coordinate multiple releases as a single, unified and automated process in Harness Continuous Delivery, replacing the need for Slack threads and spreadsheets that are still used to coordinate multi-team releases, the company said in its announcement. WIth the new capabilities, Harness enables work to move through the delivery process with shared orchestration logic and the same controls, gates, and sequence. This makes releases behave more like systems than handoffs, according to the announcement.
Like the velocity of code, organizations are creating more features than ever that need to be shipped. Executives are constantly checking in with the teams to see where the feature is.. in development, or QA or production. “You see these bottlenecks not just in terms of the delivery process and the delivery software, but even as the bottleneck even makes its way up the chain to leadership,” Rydzewski said. The feature flag capability checks each release using your existing data and decides whether to move forward, pause, or roll back.
On the Database DevOps front, which now supports Snowflake, moves schema changes through the pipelines together with with code. This, the company said, matters especially for teams building AI applications on warehouse data, where schema changes are increasingly frequent and consequential. “We’re bringing AI and Continuous Delivery to your databases, automatically handling migrations and database changes and rollbacks, but also providing that governance layer,” Rydzewski said.
Rydzewski said the release embodies an operational shift, using automation to make decisions and make sure delivery proceeds smoothly, or stop it if it isn’t.
“It all comes down to the modernization report in terms of pain points,” he said. But have organizations realized the vision of AI? “I don’t think we have,” Rydzewski opined. “We write code faster, but are we releasing faster? I don’t know that enterprises are, and so I think that’s our mission now. If we can solve this problem and unblock for AI everything after the code, I think then, and only then, can we realize the full benefit of AI.”