Debugging With Aura Agent
Blog post from Neo4j
Christoffer Bergman, Director of Engineering at Neo4j, explores the use of AI agents to enhance debugging processes for cash-handling devices, inspired by the capabilities of ChatGPT. Reflecting on his previous job in the cash management industry, Bergman describes how log files from devices such as ATMs and smart safes, which use Java-based event-driven software, are crucial yet cumbersome for debugging due to their size and complexity. By utilizing Neo4j's Aura, a cloud-based service, he simulates a data model with generated events and log files to illustrate how AI agents can identify software anomalies by analyzing patterns and discrepancies in transaction logs. In a case study, he demonstrates how an AI agent, configured with specific tools and prompts, can effectively detect a bug related to an unexpected behavior in a bill validator, leading to cash discrepancies. Bergman concludes by proposing further enhancements for integrating AI agents with source code and extending their capabilities for broader applications across event-based systems.