Hud CEO Roy Adler: Runtime Intelligence Will Be the Next Stage of Software Operations
Artificial intelligence has dramatically accelerated software development: AI agents can now generate large volumes of production-ready code in minutes. But according to Hud startup CEO Roy Adler, the speed of code writing does not solve the main problem—ensuring this code works correctly in production. Traditional observability platforms have relied on logs, metrics, and traces for infrastructure monitoring for decades, but this approach is becoming insufficient.
AI-processed from TNW; edited by Hamidun News
Artificial intelligence has dramatically accelerated software development: AI agents for code writing are now capable of generating large volumes of production-ready code in minutes. But according to Hud startup CEO Roe Adler, accelerating code writing does not solve the main engineering problem — ensuring that this software works correctly in production.
Why old monitoring tools are not enough
For decades, observability platforms have helped teams monitor infrastructure through logs, metrics, and traces — three classic sources of data about system status. This approach was built around the assumption that a human writes code meaningfully and relatively slowly, which means that the team has time to understand the system's architecture and configure monitoring for its specific features.
- AI agents already generate large volumes of production-ready code in minutes
- The speed of code writing has increased, but the correctness of operation in production remains an unsolved problem
- Classic observability tools rely on logs, metrics, and traces
- The position is commented on by Roe Adler, CEO of Hud startup
What changes with the growth of AI development
When a significant portion of code is written by an AI agent rather than an engineer, the speed at which new code appears in the system grows faster than the speed at which teams manage to understand how that code will behave in real conditions. This creates a gap between how fast code gets into production and how deeply the team understands its behavior under load — this gap, in Adler's view, should be closed by a new class of tools that work not just with logs and metrics, but with deeper understanding of what happens during code execution.
What this means
As AI agents take on more and more code writing, the industry will likely need to reconsider the very approach to production monitoring — from passive collection of logs and metrics to more active understanding of system behavior in real time, which is what Adler is talking about by calling it runtime intelligence.
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Key Questions Answered
Why old monitoring tools are not enough?
For decades, observability platforms have helped teams monitor infrastructure through logs, metrics, and traces — three classic sources of data about system status. This approach was built around the assumption that a human writes code meaningfully and relatively slowly, which means that the team has time to understand the system's architecture and configure monitoring for its specific features. AI agents already generate large volumes of production-ready code in minutes The speed of code writing has increased, but the correctness of operation in production remains an unsol
What changes with the growth of AI development?
When a significant portion of code is written by an AI agent rather than an engineer, the speed at which new code appears in the system grows faster than the speed at which teams manage to understand how that code will behave in real conditions. This creates a gap between how fast code gets into production and how deeply the team understands its behavior under load — this gap, in Adler's view, should be closed by a new class of tools that work not just with logs and metrics, but with deeper understanding of what happens during code execution.
What this means?
As AI agents take on more and more code writing, the industry will likely need to reconsider the very approach to production monitoring — from passive collection of logs and metrics to more active understanding of system behavior in real time, which is what Adler is talking about by calling it runtime intelligence.
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