The rapid adoption of AI coding tools is changing the economics of software development. The harder question is no longer simply how quickly engineering teams can write code, but how quickly they can determine whether AI-generated changes are ready for production.
Hud says AI now generates or assists with 42% of the code developers ship, and that figure is expected to reach 65% by 2027. That shift creates a growing need for engineering systems that can evaluate code against what is actually happening in production.
ClickHouse and Hud are approaching that problem by connecting observability with runtime code intelligence. Their new integration links ClickStack, ClickHouse’s open-source observability stack, with Hud’s Runtime Code Sensor, giving engineering teams a way to connect production behavior with the code changes behind it.
“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.”
The Missing Context in AI-Assisted Coding
AI coding agents can generate changes rapidly, but source code alone does not reveal how those changes will behave under real workloads. An engineering team may know what a change is supposed to do without having the same level of visibility into what it actually does after deployment.
That is where the two products take different roles. ClickStack provides the broader operational view, helping teams identify the service, deployment, or endpoint associated with an issue. Hud works closer to the code, connecting production behavior to the functions and changes responsible for it.
The integration allows the platforms to be connected through shared trace IDs, enabling a coding agent to move from an issue in ClickStack to the corresponding code-level context 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.”
Making Production Data Part of the Release Decision
The integration is designed to extend runtime intelligence earlier into the software development process.
Hud can detect issues at the function level and provide forensic context into their causes, while ClickStack supplies the wider operational picture. Together, the systems can support pre-deployment risk assessment, release verification, automated detection and investigation, and rollback and remediation workflows.
The companies also describe runtime intelligence as a potential gate for AI-generated changes. Engineering teams can evaluate changes against real-time information about how affected code behaves in production. Higher-risk changes can be held for additional review and deeper context, while safer changes can move faster or be automatically merged.
That creates a different feedback loop for AI-assisted development. Instead of treating deployment as the point where generated code meets reality, production behavior can become part of the decision-making process before and during release.
What Happens When Something Breaks
The integration is also aimed at the other end of the development cycle: resolving problems after deployment.
Production issues can emerge from relatively small changes. A query may slow down, a code path may consume more resources, or a function may behave unexpectedly under a specific workload. Hud is designed to identify these issues at the function level, while ClickStack can provide the surrounding operational context.
The combined workflow supports release verification, automatic reversion upon regression, and agentic workflows capable of creating pull requests to address underlying code issues.
“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.”
Toward a Production-Aware AI SDLC
The companies’ approach centers on giving coding agents more context than static source code or isolated alerts can provide. Runtime operational data, function-level context, and application behavior can instead become part of the development loop.
Engineering teams can get started by installing the Hud SDK and connecting it to their ClickStack service, allowing Hud’s runtime intelligence to flow alongside the OpenTelemetry data they already collect.
As AI takes on a larger role in producing software, the next challenge may be less about generating more code and more about building systems that can understand that code in production. ClickHouse and Hud are positioning their integration around that distinction: making runtime behavior part of how AI-generated software is evaluated, deployed, and fixed.
