Ask an engineer what happens when something breaks in production. They know the sequence well. Alert fires, team assembles, someone pulls the logs, someone checks traces, and everyone starts constructing a hypothesis about what went wrong. The process takes time the system does not have, depends on data that may not have been captured, and often ends in inference rather than evidence.
Now add AI-assisted development to that scenario. Coding agents have increased the velocity at which code moves from generation to production. They have not improved what happens when that code fails. Hud is built around that problem, and the appointment of Shai Alani as Vice President of Marketing is the move that takes it to market.
What Engineers Are Actually Missing
The tools engineering teams use to understand production behavior were designed around assumptions that AI-native development has made obsolete. Observability platforms confirm that failures occurred and surface the signals associated with them. What they do not provide is function-level evidence of why a failure occurred: what the code was doing, under what conditions, at the precise moment things went wrong.
Hud calls this missing capability Runtime Intelligence: production behavior resolved to the function level, paired with forensic depth for investigation when failures occur. For an engineering team in the middle of an incident, the difference is significant. Instead of reconstructing failure scenarios from incomplete log data, teams have access to function-level evidence of what the code was actually doing under real production traffic.
That changes the debugging process. Fewer hypotheses need to be tested manually. Root cause identification is faster and more accurate. Fixes can be validated with greater confidence because there is evidence of how similar code performed under comparable conditions.
For coding agents embedded in these workflows, the impact is equally direct. An agent given runtime evidence as input can make informed diagnostic recommendations. Without it, the agent reasons from code structure alone, which is useful for generation but insufficient for diagnosis.
Leadership on the Problem
“AI has changed the speed of software creation, but production is still where code proves itself,” said Roee Adler, Co-founder and CEO of Hud. “The next major category in the AI SDLC is Runtime Intelligence: production behavior resolved to the function level, coupled with deep forensics when things go wrong, so humans and agents can understand, fix, and validate software with confidence. Shai brings the experience we need to build that category and scale Hud into a defining company for AI-native engineering teams.”
Alani connected the problem directly to what drew him to the role.
“Runtime Intelligence is the missing layer in the AI software stack,” said Shai Alani, VP Marketing at Hud. “AI has made it easy to generate code, but it has not made it any easier to stand behind that code once it is running in production, where reliability is actually decided. That gap is fast becoming one of the defining problems for AI-native engineering teams, and it is exactly the kind of category you build a company around. That is why I joined Hud, and it is the story I am excited to take to market.”
Alani’s Background in the Space
Shai Alani’s prior roles at Lightrun, Coralogix, and Aporia each required building go-to-market strategy for technically grounded products in developer observability and AI monitoring. That experience is relevant because engineering audiences evaluate tools on specifics. They identify vague positioning quickly and are skeptical of capability claims that do not hold up under scrutiny.
At Hud, Alani takes on global marketing strategy, category creation, brand, and demand generation. The category creation component reflects Hud’s core ambition: to make Runtime Intelligence the recognized term for the production evidence layer that AI-native engineering teams currently lack.
The Gap Hud Is Closing
The engineering organizations Hud is targeting are shipping AI-generated code at accelerated velocity, deploying coding agents as part of their standard workflow, and discovering that traditional investigation tools were not built for this development pace. The problem shows up every time an incident drags on because the right runtime data was not captured, or a coding agent’s fix addresses a symptom rather than a cause.
With Alani leading the marketing effort, Hud is positioned to reach those teams systematically. Runtime Intelligence is the capability they are missing. Getting them to recognize it by that name is the work now underway.
