Coding agents have learned to write software quickly, but they have had no way of seeing what happens to that software once it ships. Hud is changing that with agentic rollouts, a new capability that validates AI-written changes against live production behavior and sends what it learns back to the agent that wrote the code.
The launch serves two purposes at once. In the near term, agentic rollouts let coding agents move changes from pull request to production at the speed they write them, with Hud confirming each step. Over the longer term, every rollout becomes a lesson, giving agents a feedback loop from production that helps them improve with each change they make. Both purposes rest on the same foundation: function-level runtime context.
Agents That Cannot See Production
The central limitation Hud set out to address is visibility. Agents can plan, write, and test code and open a pull request in minutes, yet they remain blind to production. A change written in minutes can wait for days until enough confidence is built to ship it. They cannot observe how their code runs once it is live, so teams hesitate to let them ship, and the agents have no way to learn from real-world results.
Roee Adler, Co-Founder and CEO of Hud, described the gap in terms of where agents already operate and where they do not:
“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.”
Giving Agents Sight of Runtime
Hud’s approach begins with its runtime code sensor. Added through a one-line SDK, the sensor automatically learns how backend services behave in production, down to the function level. That baseline becomes common ground for agents and engineers.
With agentic rollouts, the baseline informs every stage of a release, and Hud analyzes each change against production before, during, and after the rollout. Before the merge, Hud analyzes the pull request against a production impact map drawn from real parameters and code flows, classifies its production risk level, and supplies clear context for risky changes so review moves quickly. Safe changes can then merge at the pace they were written. As the new version takes traffic, Hud continuously compares its behavior with the baseline, catching regressions immediately and pinpointing the root cause and the pull request behind them. When a regression occurs, Hud’s context supports rollback, revert, and a fix to the underlying issue.
That final step is where the feedback loop closes. Hud gives the coding agent deep context on what went wrong, so the agent does not repeat the mistake. Teams, meanwhile, know the production-level impact of each change before, during, and after the rollout.
Adler continued: “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.”
Learning in a Demanding Environment
Riverside, the platform for recording and editing studio-quality audio and video, ships fast with AI, and much of its backend is real-time media code that only reveals its true behavior under production load. For code like that, production is where the real test happens.
Riverside sends every deploy through a canary with Hud’s agentic rollout and automatic rollback. Flawed deploys are detected quickly and reverted in about eight minutes, usually before a customer notices. For the Riverside team, that means less time spent worrying about deployments.
Shilo Magen, VP of R&D at Riverside, shared the team’s experience:
“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.”
Why the Loop Matters at Scale
The case for a production feedback loop grows as agents write more code. Faros AI’s 2025 vendor survey found that teams with high AI adoption merge 98% more pull requests, while pull request review time rises 91%. Engineers using AI heavily produce about twice as many pull requests, and each takes about twice as long to review. Harness’s 2025 vendor survey adds that only 6% of organizations describe their continuous delivery as fully automated.
In that setting, the merge, the review, and the watch over dashboards after each deploy all still fall to people. Coding agents have relied on that human-driven validation to reach production, which increases time to delivery and reduces productivity. Every team that adopts AI hits this wall, and it grows higher as agents write more code.
Where Hud Is Headed
Hud describes itself as the Runtime Intelligence platform for the AI software factory, and the feedback loop sits at the center of its direction. Every rollout teaches the coding agent how its changes behave in production, so each new change ships with less risk than the one before. Hud’s runtime code sensor closes the loop between the agent and what happens in the real world, and when a change causes a regression, Hud rolls it back or flags it and gives the agent the evidence it needs to fix it. For coding agents, production turns from a blind spot into a source of instruction.
