BugScout AI

BugScout AI is an intelligent issue detection and resolution system that uses a four-agent LLM architecture to automatically identify, classify, and suggest fixes for web application issues from real-time user session data. The system ingests live session replay data from PostHog, cleans and stores it in NeonDB, vectorizes it in ChromaDB, and runs an Issue Monitoring Agent to detect exceptions, rage clicks, dead clicks, and UX friction.

• Real-Time Issue Detection: Automatically detects issues from PostHog session replays—exceptions, rage clicks, dead clicks, UX friction—and classifies them using PostHog taxonomy
• Multi-Agent LLM System: Four specialized agents (Issue Monitoring, Solution, Self Learning, Codebase Crawler) work together for detection, fix generation, knowledge summarization, and code location
• Instant Notifications: Sends email and Slack alerts the moment high-friction UX issues are detected, with confidence scores and links to view/fix

• Frontend: Next
• LLM: OpenAI GPT-4o-mini for all four agents (Issue Monitoring, Solution, Self Learning, Codebase Crawler) with JSON mode and tuned token limits
• Embeddings: OpenAI text-embedding-3-small (1536-dim), batch processing; used for monitoring, issues, logs, and posthog_events in ChromaDB

BugScout AI delivers measurable gains over manual review and generic LLM baselines: +31% issue detection, +93% code location accuracy, +50% developer satisfaction, 99% reduction in time to resolution. The system is in MVP stage, testing with a partner startup; multiple startups have expressed interest.