KQL Templates — GenAI Trace Query Reference

Ready-to-use KQL templates for querying GenAI OpenTelemetry traces in Application Insights.

Table of Contents: App Insights Table Mapping · Key GenAI OTel Attributes · Span Correlation · Hosted Agent Attributes · Response ID Formats · Common Query Templates · OTel Reference Links

App Insights Table Mapping

App Insights Table	GenAI Data
dependencies	GenAI spans: LLM inference (chat), tool execution (execute_tool), agent invocation (invoke_agent)
requests	Incoming HTTP requests to the agent endpoint. For hosted agents, also carries gen_ai.agent.name (Foundry name) and azure.ai.agentserver.* attributes — preferred entry point for agent-name filtering
customEvents	GenAI evaluation results (gen_ai.evaluation.result) — scores, labels, explanations
traces	Log events, including GenAI events (input/output messages)
exceptions	Error details with stack traces

Key GenAI OTel Attributes

Stored in customDimensions on dependencies spans:

Attribute	Description	Example
gen_ai.operation.name	Operation type	chat, invoke_agent, execute_tool, create_agent
gen_ai.conversation.id	Conversation/session ID	conv_5j66UpCpwteGg4YSxUnt7lPY
gen_ai.response.id	Response ID	chatcmpl-123
gen_ai.agent.name	Agent name	my-support-agent
gen_ai.agent.id	Agent identifier	asst_abc123
gen_ai.request.model	Requested model	gpt-4o
gen_ai.response.model	Actual model used	gpt-4o-2024-05-13
gen_ai.usage.input_tokens	Input token count	450
gen_ai.usage.output_tokens	Output token count	120
gen_ai.response.finish_reasons	Stop reasons	["stop"], ["tool_calls"]
error.type	Error classification	timeout, rate_limited, content_filter
gen_ai.provider.name	Provider	azure.ai.openai, openai
gen_ai.input.messages	Full input messages (JSON array) — on invoke_agent spans	[\{"role":"user","parts":[\{"type":"text","content":"..."\}]\}]
gen_ai.output.messages	Full output messages (JSON array) — on invoke_agent spans	[\{"role":"assistant","parts":[\{"type":"text","content":"..."\}]\}]

Stored in customDimensions on customEvents (name == gen_ai.evaluation.result):

Attribute	Description	Example
gen_ai.evaluation.name	Evaluator name	Relevance, IntentResolution
gen_ai.evaluation.score.value	Numeric score	4.0
gen_ai.evaluation.score.label	Human-readable label	pass, fail, relevant
gen_ai.evaluation.explanation	Free-form explanation	"Response lacks detail..."
gen_ai.response.id	Correlates to the evaluated span	chatcmpl-123
gen_ai.conversation.id	Correlates to conversation	conv_5j66...

Correlation: Eval results do NOT link via id-parentId. Use gen_ai.conversation.id and/or gen_ai.response.id to join with dependencies spans.

Span Correlation

Field	Purpose
operation_Id	Trace ID — groups all spans in one request
id	Span ID — unique identifier for this span
operation_ParentId	Parent span ID — use with id to build span trees

Operation_Id Join (requests → dependencies)

Use requests as the hosted-agent entry point, then carry operation_Id forward as the trace key when joining into dependencies, traces, or customEvents:

let agentRequests = materialize(
    requests
| where timestamp > ago(7d)
| extend
    foundryAgentName = coalesce(
        tostring(customDimensions["gen_ai.agent.name"]),
        tostring(customDimensions["azure.ai.agentserver.agent_name"])
    ),
    agentId = tostring(customDimensions["gen_ai.agent.id"]),
    agentNameFromId = tostring(split(agentId, ":")[0]),
    agentVersion = iff(agentId contains ":", tostring(split(agentId, ":")[1]), ""),
    conversationId = coalesce(
        tostring(customDimensions["gen_ai.conversation.id"]),
        tostring(customDimensions["azure.ai.agentserver.conversation_id"]),
        operation_Id
    )