Agent Loop Detection via Trace Visualization
criticalAI agents may execute self-looping behavior that is invisible in raw logs but detectable through Phoenix's graph-based trace visualization. These loops inflate latency and token costs while degrading user experience.
Monitor agent graph visualizations for cyclic patterns in node traversal. Look for repeated visits to the same graph.node.id within a single trace, particularly when parent-child relationships form cycles. Track trajectory evaluations that flag inefficient paths or unexpected step counts.
Implement agent trajectory evaluations using LLM-as-a-Judge to assess the sequence of tool calls and detect loops or unnecessary steps. Review agent routing logic and add explicit loop detection/prevention guards. Use Phoenix's Agent Graph visualization to identify the specific nodes involved in loops and refactor agent decision logic to follow expected 'golden paths'.