Generative AI-Assisted Application Analytics for Transactional Workflows
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Solution Overview
Problem
Configuring application analytics features in complex online applications requires personnel with diverse expertise, including knowledge of transactional workflows, DevOps, and coding, making it challenging for laypeople to monitor and troubleshoot performance issues effectively.
Innovation Solution
Utilizing generative AI to translate user inputs into prompts for large language models, generating transactional workflows or funnels, and automatically configuring monitoring for these workflows, allowing laypeople to visualize and manage application analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of application analytics features is performed by experts, then configuration accuracy and reliability are improved, but operational complexity and time consumption increase
Solution Approach 1:
The system enables self-service by allowing the application itself to automatically generate and configure analytics features based on its own transactional workflows. The application's transaction data is processed by the system to automatically create monitored transactions and aggregate telemetry data, eliminating the need for expert manual configuration while maintaining high accuracy through automated analysis of actual application behavior.
2Manufacturing precision
If expert personnel are required to configure analytics features, then configuration precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system introduces an intermediary automated processing layer between the application's transaction data and the analytics configuration. This intermediary automatically translates application transaction workflows into monitored transactions and configures telemetry aggregation, eliminating the need for expert personnel while maintaining configuration precision through systematic automated processing.
Solution Approach 2:
The application automatically generates its own analytics configuration through the system's automated processing of transactional workflows. The system extracts transaction milestones, identifies monitored transactions, and configures telemetry aggregation without requiring external expert intervention, thereby improving ease of operation while maintaining precision.
3Adaptability or versatility
If multiple expertise areas are required for configuration, then analytical completeness is improved, but device complexity increases
Solution Approach 1:
The system merges multiple expertise areas into a single automated processing workflow. It combines transactional workflow analysis, monitored transaction identification, and telemetry data aggregation into one integrated system that automatically processes application data and generates comprehensive analytics configuration, eliminating the need for multiple specialized personnel while maintaining analytical completeness.
Solution Approach 2:
The system performs multiple functions through a single automated mechanism: it analyzes transactional workflows, identifies monitored transactions, aggregates telemetry data, and generates analytics configuration. This multi-functional approach replaces the need for multiple specialized experts with a universal automated system that handles all configuration aspects.
Data Source
AI summary
In one embodiment, a device generates a prompt for a large language model that returns a set of transactional milestones for a particular type of transactional workflow in an application, based on input from a user interface that indicates the particular type of transactional workflow. The device identifies a set of monitored transactions in the application that match any of the set of transactional milestones and aggregates telemetry data for the set of monitored transactions into a transactional workflow of the particular type. The device also provides information regarding the transactional workflow for display by the user interface.