Elastic Agent Builder traces overview dashboard
Elastic Agent Builder ships a prebuilt overview dashboard that turns your agent trace data into ready-made operational and usage metrics. Instead of building visualizations yourself, you install one managed dashboard and see how your agents behave, including how many tokens they use, how long conversations take, which agents run most often, and where tool calls fail.
Use the dashboard to:
- Track token usage and LLM request volume across models and providers.
- Spot slow conversations and long-running agent executions.
- Find tools that fail or run slowly.
The dashboard visualizes the trace data that Elastic Agent Builder sends to your Elasticsearch deployment, giving you a view of real agent activity. First, you must configure trace collection. Then install the dashboard in each Kibana space where you want to view the data.
The overview dashboard is a single prebuilt dashboard named [Elastic] Agent Builder Overview. It is a managed dashboard, so it is read-only. To change or extend it, duplicate it and edit the copy, as described in Customize the dashboard.
You install the dashboard separately in each Kibana space, and each copy shows only that space's trace data.
The dashboard groups its panels into four areas:
- Token Usage & Cost: Input and output tokens by model, and LLM request counts by model and provider.
- Conversation Volume & Latency: How many conversation rounds ran and how long they took, including average, 95th percentile, and maximum duration.
- Agent Execution: How often each agent ran and how long it took, broken down by agent.
- Tool Call Frequency & Errors: How often tools were called, their success and error rates, average tool duration, and the most-used tools.
When trace data is flowing, the dashboard looks like this:
Before you install the dashboard:
- Make sure trace collection is on for the space and the setting is saved. It is on by default. The Install Dashboard button appears only after trace collection is enabled and saved. For details, refer to Collect agent traces.
- Make sure you can read the trace data, otherwise the panels have no data to show. For the required privileges, refer to Read trace data.
- Make sure you can manage Kibana advanced settings. Installing and uninstalling the dashboard requires this privilege.
- Install the dashboard in each Kibana space where you want it. It is not shared across spaces.
The overview dashboard is not installed automatically. Install it once per Kibana space.
- Go to Management → GenAI Settings.
- In the Agent Builder Traces section, confirm that Collect conversation traces is on and saved.
- Select Install Dashboard.
To open the dashboard, select View Dashboard, or open Dashboards and select [Elastic] Agent Builder Overview.
Repeat these steps in each space where you want the dashboard.
The dashboard is not restored automatically, including in a new space or after you remove it. If it is missing, open the Agent Builder Traces section and select Install Dashboard again.
To remove it, select the arrow next to View Dashboard, then select Uninstall dashboard.
The overview dashboard is managed, so you cannot edit it directly. To build your own version:
- Open the dashboard.
- Duplicate it.
- Edit and save the copy.
Because the original is managed, Elastic can ship improvements to it without overwriting your copy.
The dashboard panels are ES|QL queries over your trace data. To build your own visualizations in Dashboards, Lens, or Discover, query the trace data stream and filter by span type and attribute.
The dashboard's panels query span data from the traces-agent_builder.otel-* data stream, where each document is a span. The dashboard identifies the kind of work a span represents from its span.name, and reads generative AI details from the span attributes. For the trace data stream and the read privileges, refer to Read trace data.
Each document is a span. Filter on the span.name field to select a kind of agent activity. The dashboard matches span names by prefix.
| Agent activity | Filter |
|---|---|
| LLM requests, tokens, model, and provider | span.name LIKE "chat *" |
| Conversation rounds (volume and latency) | span.name LIKE "invoke_agent *" and attributes.elastic.inference.span.kind == "CHAIN" |
| Agent executions | span.name LIKE "invoke_agent *" and attributes.elastic.inference.span.kind == "AGENT" |
| Tool calls | span.name LIKE "execute_tool *". For failures only, add status.code == "Error" |
These fields carry the details the dashboard aggregates. Generative AI attributes use the attributes. prefix.
| Field | Description |
|---|---|
attributes.gen_ai.usage.input_tokens |
Input tokens sent to the model |
attributes.gen_ai.usage.output_tokens |
Output tokens generated by the model |
attributes.gen_ai.request.model |
Model name |
attributes.gen_ai.provider.name |
Model provider |
attributes.gen_ai.agent.id |
Agent identifier |
attributes.elastic.inference.span.kind |
On invoke_agent spans, separates conversation rounds (CHAIN) from agent executions (AGENT) |
name |
Span name. On execute_tool spans it is execute_tool <tool-id>, for example execute_tool platform.core.list_indices. For the bare tool id, use attributes.gen_ai.tool.name |
duration |
Span duration in nanoseconds (root field). Divide by 1,000,000,000 for seconds |
status.code |
Span status, for example Error (root field) |
@timestamp |
When the span started |
Use these as starting points, and test them on your own data. They query one space. Replace default in traces-agent_builder.otel-default with your space id. To query across all spaces at once, use the traces-agent_builder.otel-* wildcard, which combines data from every space.
Total input and output tokens by model and provider:
FROM traces-agent_builder.otel-default
| WHERE span.name LIKE "chat *"
| STATS
input_tokens = SUM(TO_LONG(attributes.gen_ai.usage.input_tokens)),
output_tokens = SUM(TO_LONG(attributes.gen_ai.usage.output_tokens))
BY model = attributes.gen_ai.request.model,
provider = attributes.gen_ai.provider.name
| SORT input_tokens DESC
Tool calls and errors by tool:
FROM traces-agent_builder.otel-default
| WHERE span.name LIKE "execute_tool *"
| STATS
calls = COUNT(*),
errors = COUNT(*) WHERE status.code == "Error"
BY tool = name
| SORT calls DESC