Trace Forwarding

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Overview

Trace Forwarding lets your organization push OpenTelemetry (OTLP) trace data from Elementum environments to external platforms — observability tools, AI evaluation suites, or any system that accepts OTLP over gRPC or HTTP. You can configure multiple endpoints per channel to route traces to different teams or platforms in parallel. Go to Organization Settings > Platform > Trace Forwarding. Only Organization Administrators can view and modify these settings.

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Trace channels

Two independent channels are available:

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Quick start: add your first endpoint

1. Go to Organization Settings > Platform > Trace Forwarding.
2. Click Add General Endpoint or Add GenAI Endpoint depending on the type of traces you want to forward.
3. Enter a Name, your Endpoint URL, and confirm the Protocol matches what your destination platform expects.
4. Select an Authorization Type and fill in the required credentials.
5. Optionally, add Resource Attributes to tag every span with metadata such as environment or team.
6. Click Save & Test. A green Message delivered result confirms the endpoint is reachable.
7. Click Enable to activate the endpoint.
8. Go to Organization Settings > Platform > Environments, click Configure on an environment card, then click Edit Environment, select your endpoint under Trace Forwarding, and click Save Changes.

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Add or edit an endpoint

Click Add General Endpoint or Add GenAI Endpoint to open the endpoint dialog. To edit an existing endpoint, click the pencil icon in its row. Both actions open the same dialog.

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Connection

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Authentication

Select one of four options from the Authorization Type dropdown:

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Optional fields

• Additional headers — Extra HTTP headers attached to every trace export request beyond those required for authentication. Click + Add Header to add key-value pairs.
• Resource attributes — Key-value pairs added to the OTLP resource on every exported span. Use these to tag trace data with environment, team, or deployment metadata that your external platform can filter on (e.g. environment = production, team = ai-platform).

Click Save & Test to save the endpoint and verify connectivity immediately. General Trace endpoints also offer a Save option that skips the connection test. GenAI endpoints require Save & Test — connectivity must be verified before the endpoint is saved.

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Manage endpoints

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Test a connection

A connection test sends a sample OTLP trace to the configured endpoint. You can run a test two ways:

• Click Save & Test when creating or editing an endpoint.
• Click the send icon (Test Connection) in the endpoint list row.

The test times out after 30 seconds. A result modal shows latency, HTTP status code, trace ID, and span ID on success, or an error message and guidance on failure. From the modal you can Edit Connection, toggle Enable / Disable, or Cancel to close.

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Assign to an environment

After configuring and enabling an endpoint, assign it to one or more environments. Traces are only forwarded from environments where an endpoint is assigned.

1. Go to Organization Settings > Platform > Environments.
2. Find the environment you want to forward traces from and click Configure on its card, then click Edit Environment.
3. In the Trace Forwarding section, select your configured endpoint from the list.
4. Click Save Changes.

Repeat for each environment you want to forward traces from.

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Delete an endpoint

1. Click the trash icon in the endpoint list row.
2. Confirm the deletion in the dialog that appears.

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Troubleshooting

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GenAI attribute reference

View OpenTelemetry semantic conventions used in Elementum AI spans

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LLM call spans

These attributes appear on spans representing a single call to a language model. Span names follow the pattern chat <model>.

Attribute	Type	Description
gen_ai.system	string	The LLM provider (e.g. aws.bedrock, openai, anthropic).
gen_ai.request.model	string	The model name that was requested.
gen_ai.response.model	string	The model name that actually responded.
gen_ai.usage.input_tokens	integer	Number of tokens in the prompt.
gen_ai.usage.output_tokens	integer	Number of tokens in the completion.
gen_ai.prompt	string (JSON)	Input messages as a JSON array of {role, content} objects.
gen_ai.completion	string (JSON)	Output messages as a JSON array of {role, content} objects.
gen_ai.operation.name	string	Always "chat" for LLM call spans.
gen_ai.response.finish_reasons	string[]	Why the model stopped generating (e.g. ["stop"]).
gen_ai.response.id	string	The provider’s unique response identifier.

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Tool execution spans

These attributes appear on spans representing an agent executing a tool. Span names follow the pattern execute_tool <tool_name>.

Attribute	Type	Description
gen_ai.tool.name	string	The function name of the tool that was called.
gen_ai.tool.call.id	string	Links this execution back to the LLM’s tool call request.
gen_ai.operation.name	string	Always "execute_tool" for tool execution spans.

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Span tree structure

Traces from Elementum AI operations are organized into a span hierarchy:

Interaction type	Span structure
Single LLM call	root → chat
Tool-calling agent (one round)	root → chat → tool → chat
Multi-step agent (multiple rounds)	root → chat → tool → chat → tool → chat → …