ClickHouse and Hud Build a Runtime Feedback Loop for AI-Generated Software
August 13, 2026 – 4:17 pm
Credit: ClickHouse / Hud Build
TL;DR
Hud asserts that AI generates or assists with 42% of shipped code, a figure projected to rise to 65% by 2027. Its new partnership with ClickHouse’s ClickStack connects function-level runtime intelligence with infrastructure-wide observability, establishing a feedback loop where AI-generated changes are evaluated before deployment, verified post-release, and automatically investigated or rolled back if behavior deviates. monday.com is an early adopter of this combined stack.
The increasing adoption of AI in software development is shifting the bottleneck for engineering teams. While code generation speeds up, reviewing its potential impact, validating releases, and addressing unexpected behavior still requires context from production.
Hud claims that AI now generates or assists with 42% of the code developers ship, a figure expected to reach 65% by 2027. The company’s new collaboration with ClickHouse is aligned with this shift, bringing runtime data into workflows that assess and manage AI-generated changes.
The integration links ClickStack, ClickHouse’s open-source observability stack, with Hud’s Runtime Code Sensor. Together, these tools are intended to help engineering teams:
- Assess changes before deployment
- Verify them after release
- Investigate issues when production behavior changes
“AI is accelerating how quickly teams can generate code, but safely shipping it with high confidence requires production context,” stated 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.”
A Broader View of Production Behavior
The integration combines two distinct perspectives on application behavior. ClickStack provides visibility across applications and infrastructure, aiding teams in identifying the service, deployment, or endpoint associated with an issue.
Hud, meanwhile, operates closer to the code, tracking function-level behavior and correlating production activity with the functions and code changes responsible for it. The platforms can be connected via a coding agent utilizing shared trace IDs, enabling teams to move from an issue in ClickStack to relevant code-level context in Hud.
“Our users already trust ClickHouse to store and query their OpenTelemetry 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.”
This connection brings production data into play earlier in the development process. Rather than relying solely on runtime information for troubleshooting, teams can leverage it during decision-making regarding how AI-generated changes should progress through development and deployment.