Software development teams are increasingly using AI to generate and modify code, but faster code production is creating a different challenge: determining whether those changes are safe to deploy.

Hud says AI now generates or assists with 42% of the code developers ship, with that share expected to reach 65% by 2027. As coding agents produce changes at greater speed, engineering teams need more than static code analysis to understand how those changes could behave in real production environments.

ClickHouse and Hud are addressing that gap through a new integration connecting ClickStack, ClickHouse’s open-source observability stack, with Hud’s Runtime Code Sensor. The companies say the combination is designed to support investigation, review, deployment, and remediation workflows across the AI software development lifecycle.

“AI is accelerating how quickly teams can generate code, but safely shipping it with high confidence requires production context,” said May Walter, CTO of Hud. “Hud and ClickHouse bring real production behavior at an unparalleled breadth and depth, so teams can build a production-aware AI SDLC: gating changes before they ship, proactively verifying them once deployed, and fixing issues as they arise - all using real runtime truth. Together with ClickHouse, we are bringing that intelligence across the entire AI SDLC.”

Bringing Two Views of Production Together

The integration combines ClickHouse’s broad observability capabilities with Hud’s code-level runtime intelligence.

ClickStack helps engineering teams identify the service, deployment, or endpoint associated with an issue across applications and infrastructure. Hud focuses on the functions and code changes behind that behavior, providing deeper context into what is happening at the code level.

The two systems can be connected through shared trace IDs used by coding agents. This allows an engineering workflow to move from an issue identified through ClickStack to the corresponding code-level information in Hud.

“Our users already trust ClickHouse to store and query their Open Telemetry data at scale,” said Mike Shi, Head of Observability of ClickHouse. “The shift now underway is from simply watching systems or investigating issues to using that data to make day-to-day engineering decisions. Hud connects observability data to the code and changes behind it, making the entire stack more useful for teams building and shipping software with AI.”

That connection is particularly relevant when a production problem originates with a relatively small code change. A slower query, increased resource consumption, or unexpected function behavior can require engineers to correlate operational signals with the specific code responsible.

Hud’s Runtime Code Sensor is designed to detect production issues at the function level and provide forensic context around their causes. ClickStack supplies the broader operational view, giving teams visibility across the application.

Together, the platforms are intended to support workflows that extend beyond simply identifying a problem. The integration supports pre-deployment risk assessment for code changes, release verification, automatic reversion following regressions, automated investigation, and agentic workflows that can create pull requests containing code-level fixes.

The same runtime information can also be used before deployment. Higher-risk AI-generated changes can be held for additional review and provided with deeper context, while safer changes can move faster or be automatically merged.

That approach positions runtime intelligence as a potential control point in AI-assisted development. Instead of evaluating generated code separately from production behavior, teams can use information about how affected code actually performs under real conditions.

A Different Model for AI-Assisted Development

For engineering organizations adopting coding agents, the integration reflects a broader change in how development workflows can be structured. Coding agents can work with more than source code and isolated alerts; they can also use operational data, function-level information, and application behavior accumulated from production.

“Like every modern engineering organization, a growing share of our code is now written with AI,” said Rom Kadria, Senior Software Engineer, monday.com. “We write code much faster, but the challenge has shifted to shipping just as quickly while maintaining confidence that new code won’t cause harm. ClickHouse gives us the wide operational picture at scale, while Hud gives us the runtime intelligence and next-level introspection needed to evaluate and ship AI-generated code confidently. When issues do arise, combining ClickHouse and Hud allows us to triage and resolve them quickly. For a company building with AI, that combination is the obvious choice.”

The companies say this production-aware approach can span the lifecycle of an AI-generated change: assessing it before deployment, verifying its behavior during rollout, and investigating or fixing issues after deployment.

Connecting the Development Loop

Engineering teams can get started by installing the Hud SDK and connecting it to their ClickStack service. Hud’s runtime intelligence then operates alongside the OpenTelemetry data already being collected.

The integration ultimately brings observability and code-level runtime intelligence into the same workflow. As AI-generated code becomes a larger part of software development, ClickHouse and Hud are positioning production data as a source of context not just for troubleshooting, but for deciding how confidently teams can build, deploy, and maintain that code.

**This story was published on HackerNoon under our Business Blogging Program