self-diagnosis

What problem does it solve?

Monitors AI agent ecosystems to detect health issues early, identify anomalies, and locate root causes before they cause outages.

Core Features & Use Cases

• Metric collection across Daemon, Agent, and MCP components (CPU, memory, disk, latency)
• Anomaly-pattern detection (spike, drift, cascade, recurrence) to trigger proactive alerts
• Dependency-chain tracing to map failures from Daemon to external services
• Log analysis for error extraction and hotspot identification
• Use Case: In production, automatically surface root causes for high-latency tasks and failed agents

Quick Start

Run a health-check pass across active agents and report any detected anomalies.