AI agents running in production often fail in subtle ways after hours or days of operation. Current tools focus on model metrics, not agent-level behavior.
Create a hosted dashboard that collects agent actions, memory state, and API calls. Visualize decision paths and highlight anomalies.
Sell to engineering teams deploying AI agents in customer-facing applications. Helps reduce debugging time and improve reliability.
MVP could just log agent actions to a timeline. Biggest risk is agent frameworks building this in-house.