Hud Introduces Agentic Rollouts To Give AI Agents A Production-Level View Of Their Code

New York, United States, October 29th, 2026, FinanceWire

AI coding agents can generate code and open pull requests at a pace that would have been difficult for engineering teams to sustain manually. The next challenge is giving those agents enough information about live applications to help determine whether their changes are safe to release.

Hud is addressing that problem with agentic rollouts, a new capability that uses function-level runtime intelligence to evaluate code changes before deployment, monitor their behavior in production and help recover when a release causes a regression.

The launch expands Hud's focus beyond understanding how backend software behaves. The company wants that information to become part of the delivery process itself, helping coding agents and engineers make decisions based on how an application actually runs.

Why Production Context Is Different

A successful test run does not guarantee that a software change will behave as expected after deployment. Production environments introduce real traffic, application dependencies and operating conditions that can expose problems not apparent during development.

For human-led engineering teams, this uncertainty is addressed through a combination of code review, automated testing, staged releases and monitoring. But as AI agents increase the volume of changes being submitted, these processes face additional pressure.

Faros AI's 2025 research found that teams with high AI adoption merged 98% more pull requests, while pull request review time rose 91%. The results point to a possible disconnect between the productivity gains associated with AI coding and the capacity to review the resulting work.

Harness also reported in 2025 that just 6% of organizations described their continuous delivery as fully automated.

Together, these findings help explain why faster code generation does not automatically produce faster releases. Engineering teams need ways to understand the likely impact of changes and determine whether new versions behave as expected once deployed.

Building A Runtime Baseline

Hud's approach begins with a runtime code sensor that can be added through a one-line software development kit (SDK). The sensor learns how backend services behave at the function level, establishing a baseline against which future changes can be evaluated.

When an engineer or coding agent opens a pull request, Hud analyzes the proposed modification against a production impact map built from real parameters and code flows. It assigns a production risk classification and provides additional context for changes that may warrant closer review.

The system continues working after the merge. As a new version begins receiving production traffic, Hud compares its behavior with the established baseline and looks for regressions.

This function-level visibility is intended to make the resulting diagnosis more actionable. Instead of simply alerting engineers that something has gone wrong, the platform can help connect the unexpected behavior to the code and pull request responsible.

If a regression occurs, Hud can roll back or revert the release and provide the coding agent with the runtime evidence needed to address the underlying issue.

From Monitoring to a Feedback Loop

The distinction is that Hud is not positioning runtime intelligence solely as a tool for engineers investigating incidents. It wants production information to become part of how coding agents learn from the consequences of their changes.

That could change the relationship between development and operations. A deployment would no longer be treated as the end of a coding task, but as another stage in which an agent's work is evaluated against actual behavior.

Roee Adler, co-founder and CEO of Hud, sees that connection as necessary for expanding the autonomy of AI software development. “AI already changed how code gets written," said Roee Adler, Co-Founder & CEO, Hud. "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.”

A Practical Application in Real-Time Media

Riverside, a platform for recording and editing studio-quality audio and video, is using the technology with canary deployments and automatic rollback. Its real-time media backend makes production behavior particularly important, since some regressions only become apparent under actual load.

Hud says Riverside detects and reverts flawed deployments in about eight minutes, usually before customers notice.

“Our engineers use AI agents every day, and writing code is no longer the bottleneck," said Shilo Magen, VP of R&D, Riverside. "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.”

Making Agents More Production-Aware

Hud says agentic rollouts are available to all its customers. Engineering teams at monday.com, ZoomInfo, AppsFlyer and Lemonade also use the company's platform.

The larger ambition is to make runtime evidence a standard input for AI-driven software delivery. As coding agents take on more development work, the ability to assess their changes in production could become a key factor in determining how much of the release process can be safely automated.

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