Monitor AI agent run health and costs
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.
How it works
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.
Prompt
Paste this into an agent that has the servers below connected. Adjust the names and dates to your own.
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.MCP servers it needs
Source
The vendor page this playbook is drawn from, and the passage that supports it.
Agent Ping pages you when a run breaks its pattern, and it takes one line to start.Agent Ping: AI Agent Observability, Cost and Uptime