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docs: fix dead entry points, add first-agent tutorial, explain unexplained features (#489)

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* docs: fix dead entry points, add first-agent tutorial, explain gathered_context, VAD, E.164, Langfuse, Pipecat

* docs: fix broken anchors, correct HTTPS/VAD wording per review

* docs: tighten HTTPS wording and soften single-node claims per bot review
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docs/core-concepts/context-and-variables.mdx
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@ -89,11 +89,42 @@ You are speaking with the caller at {{caller_number}}.
### gathered_context
Data the agent collects *during* the call. You configure what to extract in the agent node's extraction settings — each variable has a name, type, and a prompt that tells the LLM what to look for.
Data the agent extracts *during* the call — the opposite direction of `initial_context`. Use it to turn a conversation into structured data: what the customer wants, whether they confirmed something, a value they gave you out loud.
#### How it gets populated
Turn on **extraction** on an [Agent](/voice-agent/agent) or [End Call](/voice-agent/end-call) node and define one or more variables to extract. Each variable has:
| Field | Description |
|---|---|
| `name` | The key it will appear under in `gathered_context` |
| `type` | `string`, `number`, or `boolean` |
| `prompt` | A natural-language description of what to look for, e.g. *"Did the customer confirm the appointment?"* |
<img src="../images/extracted_variables.png" alt="Extracted variables" style={{border: "1px solid #d1d5db", borderRadius: "8px", maxWidth: "100%"}} />
`gathered_context` is returned in the run record after the call completes and is available in [webhook payloads](/developer/webhooks) for downstream processing. It is **not** available as a template variable in Agent prompts — prompts can only reference `initial_context` fields.
When the conversation reaches that node, the LLM reads the transcript so far and fills in each variable based on its `prompt`. If a value can't be determined from the conversation, the variable is left empty rather than guessed — leave the `prompt` specific enough that the LLM knows exactly what counts as a match.
You can add extraction to more than one node. Each node's extracted variables are merged into the same `gathered_context` object as the call progresses, keyed by `name` — reuse a `name` at a later node if you want to overwrite an earlier value.
#### How to reference it downstream
`gathered_context` is **not available in Agent prompts** — a prompt can only reference `initial_context` fields, because extraction typically happens after the conversation that would use it. To act on extracted data, send it out via a node instead:
| Where | Syntax | Notes |
|---|---|---|
| [Webhook node](/voice-agent/webhook) payload | `{{gathered_context.field_name}}` | Prefixed, since the payload template can also reference `initial_context` |
| [Run record](/developer/webhooks#payload-context-variables) (API / dashboard) | `gathered_context` object | Returned after the run completes, alongside `recording_url` and `transcript_url` |
```json
{
"customer": "{{initial_context.customer_name}}",
"resolution": "{{gathered_context.resolution}}",
"callback_requested": "{{gathered_context.wants_callback}}"
}
```
See [Webhook Payloads](/developer/webhooks) for the full list of variables available alongside `gathered_context` in a payload template.
## Data flow example