In AI-assisted engineering, writing code is no longer the bottleneck. The holdup is the merge. A pull request waits for someone willing to approve it, because it is difficult to trust an agent to ship without breaking production.
Hud has introduced agentic rollouts to resolve that holdup with production data. The new capability lets coding agents move a change from pull request to production while Hud validates the change against real runtime behavior before the merge, during the rollout, and after it. Safe pull requests ship at the pace agents write them. Regressions are detected, rolled back or reverted, and turned into feedback for the agent that wrote the code.
Accountability Is the Sticking Point
AI is now part of almost every engineer’s day. Agents plan, write, test, and open pull requests within minutes. Triage has agents too. Yet the merge and the rollout remain human responsibilities. That reliance on human-driven validation increases time to delivery and reduces productivity.
The reason is accountability. Handing a coding agent responsibility for the stability and reliability of production is difficult, and agents have had no visibility into how their code behaves once it ships. So pull requests wait for a willing reviewer, approvals happen under time pressure, and someone keeps watch over dashboards after every deploy.
Every team that adopts AI runs into this wall, and the more code agents write, the higher it gets.
Roee Adler, Co-Founder and CEO of Hud, has heard the same concern from peers across the industry.
“AI already changed how code gets written. The bigger problem now is getting that code into production safely, at the speed agents write it. Every engineering leader I talk to is trying to solve this problem. Coding and triage have agents, but the merge and rollout still run on people, because it’s scary to give coding agents accountability over the stability and reliability of the production environment. That’s the missing link in the AI software factory, and it’s the problem we built Hud to solve.”
What Leaders Gain From Agentic Rollouts
Agentic rollouts move merge and release decisions onto evidence. Hud’s runtime code sensor, a one-line SDK, learns how backend code behaves in production at the function level. That shared runtime context becomes the reference point for agents and engineers alike.
Adler described the shift this way: “With agentic rollouts, agents and engineers work from the same runtime context, at the function level, so a change is merged to production based on evidence. When something does regress, it’s immediately detected and fixed, but more importantly the agent that wrote it gets a feedback loop to learn and improve.”
In operational terms, leaders get three layers of assurance. Before the merge, each pull request is analyzed against a production impact map using real parameters and code flows, and assigned a production risk level, with clear context supplied for risky changes to speed review. During the rollout, the new version’s behavior is compared against the function-level baseline, so regressions surface immediately along with their root cause and originating pull request. After a regression, Hud’s context supports rollback, revert, and a fix to the underlying issue, and the coding agent receives the context it needs to avoid a repeat.
The cumulative benefit for leadership is knowing the production-level impact of every change at every stage of its release, before, during, and after.
The Productivity Math
For leaders weighing how AI affects delivery, the numbers show why the release stage deserves attention. According to Faros AI’s 2025 vendor survey, teams with high AI adoption merge 98% more pull requests, and pull request review time rises 91%. Engineers who use AI heavily produce about twice as many pull requests, each taking about twice as long to review. On the delivery side, Harness’s 2025 vendor survey found that only 6% of organizations describe their continuous delivery as fully automated. The fastest part of engineering now feeds directly into the slowest, and the merge queue is where that shows first.
Hud built agentic rollouts to bring the speed of shipping closer to the speed of coding, so agents can ship as fast as they write when a pull request is safe to merge.
A Reference Customer
Riverside, the platform for recording and editing studio-quality audio and video, shows how this works in a production environment where small issues carry large consequences. The company ships fast with AI, and much of its backend is real-time media code that only shows its true behavior under production load. Every deploy goes through a canary with Hud’s agentic rollout and automatic rollback, and flawed deploys are detected quickly and reverted in about eight minutes, usually before a customer notices.
Shilo Magen, VP of R&D at Riverside, spoke to the benefit for his organization:
“Our engineers use AI agents every day, and writing code is no longer the bottleneck. The real challenge is shipping that code to production with confidence. For a real-time media platform like Riverside, even a small regression can have a major impact on the user experience. With Hud, we can automatically detect regressions and roll back problematic releases in minutes. Our engineers now spend less time worrying about deployments and more time building.”
For a leader, the Riverside example translates into recovery from a flawed release measured in minutes and an engineering team focused on building.
Hud analyzes every change against production before, during, and after the rollout. The longer-term direction is a feedback loop. Every rollout teaches the coding agent how its changes behave in production, which means the next change ships with less risk than the last. With agentic rollouts, teams can ship to production autonomously by drawing on runtime context.
