Revolutionizing Triage: How Hud’s Sensor Technology Reduced Time to 10 Minutes

Enhancing AI-Generated Code Monitoring with Runtime Sensors

In today’s tech landscape, engineering teams are leveraging AI agents more than ever to generate code. However, a significant challenge arises when this AI-generated code is deployed into production environments.

The main issue does not lie in the quality of the AI-generated code itself but rather in the limitations of traditional monitoring tools. These tools often struggle to provide the detailed, function-level data that AI agents require to comprehend how code behaves in complex production settings. Without this crucial context, AI agents are unable to identify issues or create fixes that align with real-world production scenarios.

Startup Hud aims to address this challenge with the launch of its runtime code sensor. This sensor operates alongside production code, automatically tracking the behavior of each function to provide developers with insights into actual deployment performance.

Roee Adler, CEO and founder of Hud, highlighted the importance of understanding code behavior in production environments, especially in the era of AI-accelerated development. He emphasized the need for developers to have comprehensive insights into code performance in real-world scenarios.

Challenges Faced by Software Developers

Software developers across various engineering organizations encounter similar pain points when it comes to traditional monitoring tools. Moshik Eilon, group tech lead at Monday.com, shared frustrations with the limitations of these tools, particularly in diagnosing issues in production environments.

Eilon explained the common scenario where developers receive alerts about endpoint errors or high latencies but struggle to trace the root cause due to lack of detailed data. Manual detective work across multiple tools is often required to pinpoint the exact issue, especially for complex problems deep within a large codebase.

Similarly, Daniel Marashlian, CTO and co-founder at Drata, noted the challenge his engineers faced in spending hours on investigative tasks to identify and resolve production issues. He highlighted the need to streamline this process to focus on actual fixes rather than extensive discovery efforts.

Drata’s integration with various external services added complexity to issue investigations, requiring engineers to trace behavior across a wide codebase encompassing different modules.

Marashlian identified three key problems that led Drata to invest in runtime sensors, including the cost of context switching, alert fatigue, and the necessity to align with the company’s AI strategy.

Limitations of Traditional APMs

Enterprises traditionally rely on Application Performance Monitoring (APM) tools to monitor service performance. However, with the rapid advancement of AI-driven development and modern workflows, existing APM tools often fall short in providing the required visibility for comprehensive code monitoring.

Eilon highlighted the challenges faced by Monday.com in obtaining detailed data from APM tools like Datadog and CoreLogix, mainly due to cost constraints and low sampling rates. This resulted in missing critical data needed for debugging and performance analysis.

Traditional APM tools also require predictive capabilities, which can be limiting when developers encounter novel issues that were not anticipated. Marashlian emphasized the need for precise data in debugging complex issues within extensive codebases.

Despite evaluating various AI site reliability engineering and incident response solutions, Drata found that most tools focused on incident management rather than providing deep insights into code behavior.

Tools like error monitors capture exceptions, but Adler pointed out that simply being aware of exceptions does not offer the necessary context for AI agents to propose effective fixes.

Functionality of Runtime Sensors

Runtime sensors offer a unique approach to code monitoring by pushing intelligence to the edge where code executes. Hud’s sensor operates as an SDK integrated with a single line of code, capturing function execution data and sending aggregated insights unless anomalies occur.

When errors or slowdowns occur, the sensor automatically collects detailed forensic data, including HTTP parameters, database queries, and full execution context. It establishes performance baselines quickly and can detect significant slowdowns and outliers that traditional monitoring methods may overlook.

Eilon emphasized the value of having access to granular function data, even for underlying packages, enabling rapid identification of issues deep within the codebase.

The platform delivers data through web applications for centralized monitoring, IDE extensions for popular code editors, an MCP server for feeding structured data to AI agents, and an alerting system for issue identification without manual setup.

Integration with the MCP server is crucial for AI-assisted development, allowing engineers to query production behavior directly within their development environment.

Impact of Runtime Sensors

The transition from theoretical capability to practical impact is evident in how engineering teams utilize runtime sensors. Tasks that previously required hours or days of investigation now take minutes to resolve, thanks to the detailed insights provided by the sensors.

Eilon shared his experience of resolving complex incidents quickly with the help of runtime sensor data, highlighting the efficiency gains achieved by having immediate access to function-level production insights.

At Drata, the impact of runtime sensors was significant, leading to a substantial reduction in manual triage work and improvement in mean time to resolution. The team also saw an increase in ticket throughput and faster forensic diagnosis of issues.

Implications for Enterprises

For organizations leveraging AI coding assistants, runtime intelligence offered by sensors adds a layer of security to production deployments. This technology enables efficient issue diagnosis and resolution, reducing reliance on manual troubleshooting processes.

Eilon emphasized the importance of runtime intelligence in bridging the knowledge gap created by the increasing use of AI-generated code. Having production context directly integrated into the development environment enhances trust in the code being deployed.

For enterprises scaling AI code generation initiatives, runtime intelligence becomes essential in providing AI agents with the necessary context to generate reliable code at scale. It addresses the challenge of aligning AI assumptions with real-world production scenarios.

Organizations should evaluate the effectiveness of their current observability stack in providing granular function-level data required by AI agents. If existing tools fall short in delivering this level of insight, runtime sensors offer a sustainable solution for AI-accelerated development workflows.