AI made writing code fast. Coding agents now plan, write, and test changes and open pull requests in minutes. Shipping that code safely, however, has become the biggest bottleneck in engineering. That is because a change still has to be trusted enough to reach production. A change written in minutes can then wait for days until enough confidence is built to ship it.

Hud is addressing that shift head-on. The company has launched agentic rollouts, which allow coding agents to carry a change from pull request to production. Hud validates every step against real runtime behavior. The aim is to bring the speed of shipping closer to the speed of coding. An Industry Running at Two Speeds Every engineering team is working to build what Hud calls the AI software factory. It is a pipeline where agents handle planning, coding, and testing. The front half of that pipeline has accelerated sharply. The back half, where changes are merged, deployed, and monitored, still relies on people.

The figures reflect the imbalance. Faros AI’s 2025 vendor survey found that teams with high AI adoption merge 98% more pull requests. Pull request review time rises 91% alongside that gain. Developers on those teams touch 47% more pull requests a day.

Deployment automation has its own catching up to do. Harness reported in its 2025 vendor survey that just 6% of organizations consider their continuous delivery fully automated.

In this environment, reviewers approve pull requests they lacked the time to read in full. Someone has to watch dashboards after each deploy. As agents produce more code, the queue at the merge stage grows with it. Every team that adopts AI reaches this pressure point. Hud’s view is that agents build quickly but cannot see production. That blind spot is what holds the rest of the pipeline back.

Rollouts Designed for Agents Hud defines an agentic rollout as a rollout built for agents. The foundation is Hud’s runtime code sensor, installed with a one-line SDK. It automatically learns the baseline behavior of backend code at the function level. That baseline comes straight from production.

Until now, coding agents could not get to production without human-driven validation. That increases time to delivery and reduces productivity. With agentic rollouts, teams can ship to production autonomously by drawing on runtime context instead.

Hud applies that runtime context at each phase of a release. Prior to merging, Hud evaluates the pull request against a production impact map that draws on real parameters and code flows. It then assigns a production risk level. Risky changes arrive with clear context that shortens review.

As each new version hits production, Hud closely watches its behavior at the function level. Once it starts serving traffic, Hud measures its behavior against the baseline. It catches regressions immediately and pinpoints both the root cause and the originating pull request. If something regresses, Hud enables rollback, revert, and a fix to the underlying issue. It also feeds the coding agent the context it needs to avoid the same error going forward.

For engineering teams, the outcome is visibility into the production-level impact of every change, at every stage of its release. Agents and engineers work from the same runtime context, so a change is merged to production based on evidence.

What the Market Is Already Seeing Riverside shows the approach in practice. The platform for recording and editing studio-quality audio and video ships quickly with AI. A significant share of its backend is real-time media code that reveals its true behavior only under production load.

Every Riverside deploy runs through a canary paired with Hud’s agentic rollout and automatic rollback. Flawed deploys are caught quickly and reverted in about eight minutes, usually before customers notice anything.

Shilo Magen, VP of R&D at Riverside, framed the challenge plainly: “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.”He added: “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.”Riverside sits within a broader customer base that includes engineering teams at monday.com, Lemonade, Cyera, and Drata.

Where the Category Is Heading Roee Adler, Co-Founder and CEO of Hud, sees the merge and rollout stages as the remaining human-run link. The rest of the chain has already accelerated.

“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. 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.”Hud positions itself as the Runtime Intelligence platform for the AI software factory.

Agentic rollouts extend that platform into the release process. Its runtime code sensor closes the loop between the agent and real-world behavior. Teams can then ship to production autonomously based on runtime context. When a change causes a regression, Hud rolls it back or flags it and supplies the evidence the coding agent needs to fix it.

The longer-term direction centers on learning. Every rollout teaches the coding agent how its changes behave in production. Each new change then carries less risk than the last.