SCORPGUARD: The AI Agent Runtime Guardian

86% of enterprise AI agents fail in production. Not because of bad models, but because of broken runtime infrastructure. Container restarts erase agent memory, hallucinated data silently corrupts workflows, and runaway token costs breach budgets. SCORPGUARD is the Agent Runtime Guardian that solves this "last mile" problem. It acts as an intelligent middleware layer that wraps around any AI agent, ensuring safe, cost-effective, and reliable execution. Key Features: - Event-Sourced State Persistence: Uses PostgreSQL to checkpoint agent state, ensuring memory survives container restarts. - Dual-Layer Anomaly Detection: Layer 1 (Rule-Based) catches fatal crashes and loops in under 10ms. Layer 2 (Semantic AI) uses LLM inference to catch logical hallucinations and silent errors. - Auto-Rollback Engine: Instantly rewinds agent state to the last safe checkpoint upon detecting an anomaly, preventing cascading failures. - Real-Time Cost Monitor: Tracks token usage and USD cost per agent action. Built for the AMD Developer Hackathon ACT III, SCORPGUARD is architected to run on AMD Instinct MI300X using ROCm and vLLM (Qwen2.5). For high availability and demonstration, we implemented a robust fallback using Groq's API for ultra-fast LLM inference. The backend is powered by FastAPI, Celery, and Redis, with a real-time dark-theme command center for monitoring.