Hud CEO Roee Adler: Runtime Intelligence to Define the Next Era of Software Operations
July 7, 2026 – 4:35 pm
Image by: Hud
Artificial intelligence has dramatically accelerated software development, with coding agents now capable of producing large volumes of production-ready code in minutes. Yet while writing software has become faster, ensuring that software behaves correctly in production remains one of engineering’s biggest challenges.
For decades, observability platforms have helped teams monitor infrastructure through logs, metrics, and traces. But according to Roee Adler, CEO of Hud, the rapid rise of AI-assisted development is exposing the limitations of systems originally designed for human operators rather than autonomous coding agents.
Software Is Being Written for a Different Era
Adler believes the shift isn’t simply about adopting AI tools. Instead, it’s about recognizing that software development itself is undergoing a fundamental transformation. As he explains, “The fundamental change is that software is no longer written primarily by humans using the engineering processes we’ve refined over decades; it’s being written by coding agents who are a completely different species.”
Those agents, he says, are “fast, impatient, aggressive,” but they also lack the production context needed to consistently make safe decisions in complex environments.
Rather than slowing AI adoption, Adler believes the industry should build the infrastructure that enables coding agents to become trustworthy collaborators. In his view, the goal isn’t to replace engineers, but to equip AI with enough real-world understanding to operate confidently in production.
Faster Development Has Created a New Bottleneck
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Although AI is dramatically increasing the amount of code developers can produce, Adler argues that organizations aren’t seeing the same acceleration in overall software delivery.
The problem, he says, isn’t that engineering teams suddenly face new operational blind spots. Instead, existing review and validation processes weren’t designed for the pace of AI-generated development. As more code reaches production, confidence becomes harder to maintain. Adler notes that while individual engineers are becoming more productive, “the bottleneck shifted to the gate, which was trying to prevent bad changes from breaking the system.”
He believes engineering organizations now face two pressing questions: how to review an ever-growing volume of AI-generated code while ensuring business intent is preserved, and how to preserve institutional knowledge as fewer engineers understand every corner of increasingly AI-generated codebases.
Why Observability Falls Short for AI
Engineering teams already invest heavily in logs, metrics, traces, and application performance monitoring platforms. Those systems remain valuable for identifying when services become unhealthy, but Adler argues they weren’t designed to provide the kind of evidence AI systems require.
As he puts it, “They’re built to surface that something is wrong, not why.” While logs, metrics, and traces can help engineers investigate incidents, “agents iterating over logs” is what we have today, and it’s insufficient.