Analyze Latency — Find and Diagnose Slow Traces

Identify slow agent traces, find bottleneck spans, and correlate with token usage.

Step 1 — Find Slow Conversations

⚠️ Hosted agents: gen_ai.agent.name on dependencies holds the code-level class name (e.g., BingSearchAgent), NOT the Foundry agent name. To scope by Foundry name, use the Hosted Agent Variant below.

dependencies
| where timestamp > ago(24h)
| where customDimensions["gen_ai.operation.name"] == "invoke_agent"
| project timestamp, duration, success,
    agentName = tostring(customDimensions["gen_ai.agent.name"]),
    conversationId = tostring(customDimensions["gen_ai.conversation.id"]),
    operation_Id
| summarize
    totalDuration = sum(duration),
    spanCount = count(),
    hasErrors = countif(success == false) > 0
  by conversationId, operation_Id
| where totalDuration > 5000
| order by totalDuration desc
| take 50

Default threshold: 5 seconds. Ask the user for their latency threshold if not specified.

Step 2 — Latency Distribution (P50/P95/P99)

dependencies
| where timestamp > ago(24h)
| where customDimensions["gen_ai.operation.name"] in ("chat", "invoke_agent")
| summarize
    p50 = percentile(duration, 50),
    p95 = percentile(duration, 95),
    p99 = percentile(duration, 99),
    avg = avg(duration),
    count = count()
  by operation = tostring(customDimensions["gen_ai.operation.name"]),
     model = tostring(customDimensions["gen_ai.request.model"])
| order by p95 desc

Present as:

Step 3 — Bottleneck Breakdown

For a specific slow conversation, break down time spent per span type:

dependencies
| where operation_Id == "<operation_id>"
| extend operation = tostring(customDimensions["gen_ai.operation.name"])
| summarize
    totalDuration = sum(duration),
    spanCount = count(),
    avgDuration = avg(duration)
  by operation, name
| order by totalDuration desc

Common bottleneck patterns:

• chat spans dominate → LLM inference is slow (consider smaller model or caching)
• execute_tool spans dominate → Tool execution is slow (optimize tool implementation)
• invoke_agent has long gaps → Orchestration overhead (check agent framework)

Step 4 — Token Usage vs Latency Correlation

dependencies
| where timestamp > ago(24h)
| where customDimensions["gen_ai.operation.name"] == "chat"
| extend
    inputTokens = toint(customDimensions["gen_ai.usage.input_tokens"]),
    outputTokens = toint(customDimensions["gen_ai.usage.output_tokens"])
| where isnotempty(inputTokens)
| project duration, inputTokens, outputTokens,
    model = tostring(customDimensions["gen_ai.request.model"]),
    operation_Id
| order by duration desc
| take 100

High token counts often correlate with high latency. If confirmed, suggest:

• Reduce system prompt length
• Limit conversation history window
• Use a faster model for simpler queries

Hosted Agent Variant — Latency

For hosted agents, scope by Foundry agent name via requests then join to dependencies:

let reqIds = requests
| where timestamp > ago(24h)
| where customDimensions["gen_ai.agent.name"] == "<foundry-agent-name>"
| distinct id;
dependencies
| where timestamp > ago(24h)
| where operation_ParentId in (reqIds)
| where customDimensions["gen_ai.operation.name"] in ("chat", "invoke_agent")
| summarize
    p50 = percentile(duration, 50),
    p95 = percentile(duration, 95),
    p99 = percentile(duration, 99),
    avg = avg(duration),
    count = count()
  by operation = tostring(customDimensions["gen_ai.operation.name"]),
     model = tostring(customDimensions["gen_ai.request.model"])
| order by p95 desc