Generative AI-Assisted Telemetry Instrumentation for Custom Attributes
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Solution Overview
Problem
Conventional telemetry instrumentation methods require manual code changes and redeployments to collect custom attributes, which are labor-intensive, prone to errors, and lack scalability, making it difficult to adapt to evolving application features and operational goals.
Innovation Solution
Generative AI-assisted telemetry instrumentation uses a catalog of attributes maintained by a monitoring agent, which receives user input through a language model to generate configurations for collecting custom data without manual code changes, leveraging extensions like OpenTelemetry to dynamically add custom instrumentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual coding probes or markers into an application is used to collect custom telemetry data, then custom attributes can be collected, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The patent replaces manual mechanical code injection with an automated AI-driven system. Users interact through natural language prompts, and the system automatically generates instrumentation code, configures monitoring agents, and sets up data collection pipelines, eliminating the need for manual probe coding while maintaining custom attribute collection capabilities
Solution Approach 2:
The system enables self-service telemetry setup where users can define custom attributes through simple prompts without requiring technical expertise in instrumentation. The platform automatically handles the complex tasks of code generation, agent configuration, and data pipeline setup, allowing non-technical users to collect custom telemetry data independently
2Adaptability or versatility
If manual code changes and redeployments are performed to collect custom data, then custom telemetry can be implemented, but disruptions and delays increase
Solution Approach 1:
The patent implements dynamic telemetry configuration where custom attributes can be added and modified at runtime without requiring application redeployment. The system uses configurable instrumentation that can be updated live, allowing teams to adapt their monitoring capabilities dynamically in response to changing business needs without disrupting service
Solution Approach 2:
The system performs preliminary actions by pre-configuring instrumentation frameworks and monitoring agents before custom data collection is needed. When users need to collect custom attributes, the system can rapidly instantiate the necessary components from pre-built templates and configurations, significantly reducing the time required compared to manual code changes and redeployments
3Ease of manufacture
If standard automatic instrumentation is used, then collection and exportation of telemetry data is standardized, but custom nonstandard metrics cannot be collected
Solution Approach 1:
The patent creates a universal instrumentation framework that handles both standard and custom telemetry collection through a single unified system. The framework maintains standard compliance for broad compatibility while simultaneously supporting custom attribute collection through configurable extensions, allowing the same infrastructure to serve both standardized and customized monitoring needs
Solution Approach 2:
The system enables parameter changes in telemetry schema without requiring system reconfiguration. Users can dynamically add, modify, or remove custom attributes through configuration changes, and the system adapts the data collection and exportation processes accordingly, maintaining flexibility while preserving standardization benefits
4Ease of operation
If AI-generated configurations are used for telemetry collection, then ease of operation improves, but system complexity increases
Solution Approach 1:
The patent introduces an AI intermediary layer that translates user-friendly natural language prompts into complex instrumentation configurations. This intermediary system handles the complexity of code generation, agent configuration, and data pipeline setup, shielding users from technical complexity while delivering ease of operation through simple prompt-based interaction
Data Source
AI summary
In one implementation, a device may maintain a catalog of attributes that can be collected via a monitoring agent from an application. The device may receive, from a user interface, a prompt for input to a language model that requests collection of a particular type of data from the application. The device may generate, using the language model, a response to the prompt that includes a recommended configuration for the monitoring agent to collect the particular type of data from the application. The device may provide the response to the user interface for display.