diff --git a/agents/build/custom-llm.mdx b/agents/build/custom-llm.mdx
index 20ebeee..fcf7d75 100644
--- a/agents/build/custom-llm.mdx
+++ b/agents/build/custom-llm.mdx
@@ -95,7 +95,24 @@ Requests authenticate with `Authorization: Bearer <your API key>`. The endpoint
 
 ## Point the agent at your endpoint
 
-Set the `llm.custom` section on the agent's [configuration](/agents/build/configuration#configure-through-the-api):
+To set it up in the console:
+
+1. Open the agent's **Configuration** and click the **Model** selector.
+2. Choose **+ Custom LLM** at the bottom of the menu.
+3. Enter the base URL, the model name and your API key.
+4. Click **Run test**. It sends one streaming turn with the agent's prompt and tools, just like a live call.
+5. Click **Save and use**. The agent now uses your endpoint.
+
+**Run test** reports:
+
+- Whether the endpoint was reached.
+- Whether it streamed `delta.content`.
+- Whether it accepted the tools.
+- The time to first token.
+
+The saved endpoint appears under **Custom** in the selector. You can edit or remove it there. If you pick a platform model again, the endpoint and the stored key are removed.
+
+Through the API, set the `llm.custom` section on the agent's [configuration](/agents/build/configuration#configure-through-the-api):
 
 ```bash
 curl --request PATCH "https://api.fish.audio/v1/agent/agents/$AGENT_ID/config" \
@@ -129,11 +146,15 @@ A custom LLM never silently falls back to a platform model, because a platform m
 - After three consecutive failed generations the agent apologizes, hangs up, and the session ends with `end_reason: llm_endpoint_failure` (in the [sessions API](/agents/monitor/conversation-history#status-and-end-reason), the dashboard, and the `call.ended` webhook's `ended_reason`). Each failure also emits an `llm.endpoint_error` event on the session timeline.
 
 <Tip>
-Voice conversations are latency sensitive, so aim for a time-to-first-token under 800 ms. Turn latency is attributed per session in [conversation history](/agents/monitor/conversation-history), which lets you tell endpoint time from platform time.
+Voice conversations are latency sensitive:
+
+- Aim for a time-to-first-token under 800 ms. This is a recommendation, not a limit.
+- Reasoning models (GPT-6, o-series and similar) are often slow. For voice, turn reasoning off or use a non-reasoning model.
+- **Run test** shows the time to first token of one turn.
+- [Conversation history](/agents/monitor/conversation-history) shows turn latency per session. It separates endpoint time from platform time.
 </Tip>
 
 ## Limitations
 
 - Single Turn and Tool [agent tests](/agents/test/agent-tests) are not supported. A scripted test run refuses to execute rather than substitute a platform model for yours. Simulation tests run on your endpoint.
-- Configuration is API-only for now. A console UI comes later.
 - The prompt-level safety guardrails still ride the assembled context, but your model decides whether to honor them.