Software application development teams are changing as a result of automated code co-pilots, an increasing number of pre-configured automations and the presence of agentic services designed to facilitate the deterministic drive towards what some programming advocates call vibe coding. This means software code, at runtime, needs even greater levels of introspective analysis. Named after the heads-up-display (HUD) used in modern video games, Hud is seeking to service this need with the launch of its Runtime Code Sensor.
This technology provides real-time, function-level production insights to both software engineers and AI coding agents. It provides what the company calls “the production context” needed to build code that excels in real-world environments, not just localised test-bed or sandboxes.
Runtime Code Sensor
The Runtime Code Sensor on offer here works to produce “production awareness”, a level of code analytics that its makers say put it beyond reactive monitoring approaches. The lightweight software sensor has a comparatively small installation footprint (installation takes one minute and requires zero configuration) and can capture live function-level behaviour of applications or data services continuously, including performance metrics, errors, execution flows and dependencies.
Crucially, it can capture this information without requiring logs, traces, or manual instrumentation and then connects in real-time between service-level issues and function-level root causes, gathering and serving the granular context needed to resolve issues as they occur.
“Every software team building at scale faces the same fundamental challenge: building high-quality products that work well in the real world,” said Roee Adler, CEO and founder of Hud.” In the new era of AI-accelerated development, not knowing how code behaves in production becomes an even bigger part of that challenge. Our Runtime Code Sensor transforms the feedback loop between development and production, ensuring immediate validation and optimisation of software code – whether human-written or AI-generated.”
Built from first principles for AI consumption, the platform automatically captures the service-level and function-level data that language models need to understand how code behaves in the real world. This capability enables AI coding agents to make informed decisions based on actual production behavior rather than assumptions, dramatically improving the safety and reliability of AI-generated code.
VS Code, JetBrains or Cursor
Hud’s platform delivers production intelligence through multiple integrated capabilities. An IDE-Integrated Runtime Visibility function offers real-time production intelligence embedded directly into developer IDEs (VS Code, JetBrains, Cursor), surfacing insights where code is authored without context switching.
Adler says that his firm’s MCP Server offers structured production insights delivered directly to AI coding agents (and there is support here for Cursor, Copilot, Claude and most CLI agents), enabling production-aware code generation based on actual live behaviour. Automatic Root-Cause Identification means continuous detection of production errors and degradations with immediate alerts containing specific root causes and the context needed for rapid resolution by both human engineers and AI agents.
As we close out 2025, we don’t have a definitive steer on what it takes to control AI code in live production systems. What Hud has done in terms of shipping a lightweight product that works continuously, with immediate alerts and specific root cause analysis sounds like a good part of the ingredients needed for a successful recipe in this space… pre-runtime analysis and automated remediation would be a nice extra on top, but that will come in good time.