The user receives a detailed breakdown of why an agent run failed, including cost attribution, tool call details, and quality scoring, enabling rapid debugging of production incidents.
The agent queries the AgentPing MCP server to retrieve logs and metrics for specific agent runs. It identifies failures by checking for error states, high costs, or quality score drops. The agent then synthesizes this data to explain the root cause, such as a silent scheduler failure or a tool call succeeding at the wrong task, allowing the user to fix the issue.
- Category: Operations and infrastructure
- Effect: Read-only
- MCP servers: AgentPing MCP (https://koskamo.com/explore/agentping-mcp)
- Source: Blog: AI Agent Cost, Monitoring and Quality - Agent Ping (https://agentping.io/blog)
- Source quote: "Connect Agent Ping to any MCP-compatible assistant and ask it what failed, what a run cost, what the evaluation said, and on paid plans have it acknowledge the incident and tune the rule."
- Playbook page: https://koskamo.com/explore/use-cases/investigate-ai-agent-production-failures
Prompt: Analyze the recent agent runs in AgentPing to identify any failures. For each failure, report the cost, the specific tool calls made, and the quality evaluation score. Explain why the run failed based on the instrumentation data.