Gain real-time visibility into AI agent performance, including cost attribution per customer, run status, and output quality scores, enabling proactive detection of anomalies and drift.
The agent uses the AgentPing MCP server to query the current state of monitored AI workflows. It checks the 'Spend' view to identify cost drivers and anomalies, the 'Pulse' view to verify if scheduled runs occurred on time, and the 'Verify' view to assess output quality. Before acting, the agent should verify which agents are critical and review any open incidents or alerts to understand the context of the data.
- Category: Operations and infrastructure
- Effect: Read-only
- MCP servers: AgentPing MCP (https://koskamo.com/explore/agentping-mcp)
- Source: Agent Ping: AI Agent Observability, Cost and Uptime (https://agentping.io/)
- Source quote: "Agent Ping pages you when a run breaks its pattern, and it takes one line to start."
- Playbook page: https://koskamo.com/explore/use-cases/monitor-ai-agent-run-health-and-costs
Prompt: Check the current health and cost status of our AI agents. Specifically, identify any agents that have missed their scheduled runs or exceeded their cost baselines. Also, report on the average quality score for recent runs to ensure output consistency.