Conversation Detail — Reconstruct Full Span Tree

Reconstruct the complete span tree for a single conversation to see exactly what happened: every LLM call, tool execution, and agent invocation with timing, tokens, and errors.

Step 1 — Fetch All Spans for a Conversation

Use operation_Id (trace ID) to get all spans in a single request:

dependencies
| where operation_Id == "<operation_id>"
| project timestamp, name, duration, resultCode, success,
    spanId = id,
    parentSpanId = operation_ParentId,
    operation = tostring(customDimensions["gen_ai.operation.name"]),
    model = tostring(customDimensions["gen_ai.request.model"]),
    responseModel = tostring(customDimensions["gen_ai.response.model"]),
    inputTokens = toint(customDimensions["gen_ai.usage.input_tokens"]),
    outputTokens = toint(customDimensions["gen_ai.usage.output_tokens"]),
    responseId = tostring(customDimensions["gen_ai.response.id"]),
    finishReason = tostring(customDimensions["gen_ai.response.finish_reasons"]),
    errorType = tostring(customDimensions["error.type"]),
    toolName = tostring(customDimensions["gen_ai.tool.name"]),
    toolCallId = tostring(customDimensions["gen_ai.tool.call.id"])
| order by timestamp asc

Also fetch the parent request:

requests
| where operation_Id == "<operation_id>"
| project timestamp, name, duration, resultCode, success, id, operation_ParentId

Step 2 — Build Span Tree

Use spanId and parentSpanId to reconstruct the hierarchy:

invoke_agent (root) ─── 4200ms
├── chat (LLM call #1) ─── 1800ms, gpt-4o, 450→120 tokens
│   └── [output: "Let me check the weather..."]
├── execute_tool (get_weather) [tool: remote_functions.weather_api] ─── 200ms
│   └── [result: "rainy, 57°F"]
├── chat (LLM call #2) ─── 1500ms, gpt-4o, 620→85 tokens
│   └── [output: "The weather in Paris is rainy, 57°F"]
└── [total: 450+620=1070 input, 120+85=205 output tokens]

Present as an indented tree with:

• Operation type and name
• Duration (highlight if > P95 for that operation type)
• Model and token counts (for chat operations)
• Error type and result code (if failed, highlight in red)
• Finish reason (stop, length, contentfilter, toolcalls)

Step 3 — Extract Conversation Content from invoke_agent Spans

The full input/output content lives on invoke_agent dependency spans in gen_ai.input.messages and gen_ai.output.messages. These JSON arrays contain the complete conversation (system prompt, user query, assistant response):

dependencies
| where operation_Id == "<operation_id>"
| where customDimensions["gen_ai.operation.name"] == "invoke_agent"
| project timestamp,
    inputMessages = tostring(customDimensions["gen_ai.input.messages"]),
    outputMessages = tostring(customDimensions["gen_ai.output.messages"])
| order by timestamp asc

Message structure: [{"role": "user", "parts": [{"type": "text", "content": "..."}]}]

Also check the traces table for additional GenAI log events:

traces
| where operation_Id == "<operation_id>"
| where message contains "gen_ai"
| project timestamp, message, customDimensions
| order by timestamp asc

Step 4 — Check for Exceptions

exceptions
| where operation_Id == "<operation_id>"
| project timestamp, type, message, outerMessage,
    details = parse_json(details)
| order by timestamp asc

Present exceptions inline in the span tree at their position in the timeline.

Step 5 — Fetch Evaluation Results

See Eval Correlation for the full workflow to look up evaluation scores by response ID or conversation ID. Use gen_ai.response.id values from Step 1 spans to correlate.