docs(ai-agents): address review feedback on the chat agent guide

Describe the chat agent lifecycle as a durable session rather than a single
run, add an authorization note to the token-minting example, switch the guide
code fences to the typescript language tag, and enable telemetry on the news
verifier's claim-extraction call so it matches the guide's description.

Author: D-K-P

docs/guides/ai-agents/chat-agent.mdx

@@ -6,15 +6,15 @@ description: "Create a durable, multi-turn chat agent with chat.agent(), then ad
 
 ## Overview
 
-Build a **durable, multi-turn chat agent**. One Trigger.dev run holds the whole conversation, streams tokens to your UI, and stays alive across many back-and-forth messages. The other guides in this section are one-shot workflows (trigger a task, run a fixed sequence of LLM calls, return a result); a chat agent instead owns the session for its whole lifetime.
+Build a **durable, multi-turn chat agent**. A durable session owns the conversation, streams tokens to your UI, and stays alive across many back-and-forth messages. The other guides in this section are one-shot workflows (trigger a task, run a fixed sequence of LLM calls, return a result); a chat agent instead owns the session for its whole lifetime.
 
 [`chat.agent()`](/ai-chat/overview) handles the queuing, retries, resumability and streaming for you. You write the model call, Trigger.dev owns the session. For the full feature set (sessions, fast starts, compaction, sub-agents, the frontend transport), see the [AI chat docs](/ai-chat/overview).
 
 ## A minimal agent
 
 Define an agent with `chat.agent()`. The `run` function receives the conversation `messages` (already converted from the frontend's `UIMessage[]`) and an abort `signal`. Return a `StreamTextResult` and it's piped to the frontend automatically.
 
-```ts trigger/chat.ts
+```typescript trigger/chat.ts
 import { chat } from "@trigger.dev/sdk/ai";
 import { anthropic } from "@ai-sdk/anthropic";
 import { streamText, stepCountIs } from "ai";
@@ -45,7 +45,7 @@ export const myChat = chat.agent({
 
 A chat agent uses tools exactly like any other AI SDK agent. Declare them on the config so their results survive across turns, then pass the `tools` you receive in `run` straight to `streamText`:
 
-```ts trigger/chat.ts
+```typescript trigger/chat.ts
 import { chat } from "@trigger.dev/sdk/ai";
 import { anthropic } from "@ai-sdk/anthropic";
 import { streamText, stepCountIs, tool } from "ai";
@@ -80,7 +80,7 @@ Swap `getCurrentTime` for whatever your agent needs to do: query a database, cal
 
 The browser talks to Trigger.dev directly through the [chat transport](/ai-chat/frontend), so there's no API route to maintain. Expose two server actions (one to start the session, one to mint a session-scoped token) and pass them to `useTriggerChatTransport`, then hand the transport to the AI SDK's `useChat`:
 
-```ts app/actions.ts
+```typescript app/actions.ts
 "use server";
 
 import { auth } from "@trigger.dev/sdk";
@@ -89,6 +89,9 @@ import { chat } from "@trigger.dev/sdk/ai";
 export const startChatSession = chat.createStartSessionAction("my-chat");
 
 export async function mintChatAccessToken(chatId: string) {
+  // Authorize the caller for this chatId before minting: confirm the logged-in
+  // user owns this session (e.g. look it up in your database). Otherwise anyone
+  // who learns a session ID could mint read/write access to it.
   return auth.createPublicToken({
     scopes: { read: { sessions: chatId }, write: { sessions: chatId } },
     expirationTime: "1h",

docs/guides/ai-agents/overview.mdx

@@ -78,7 +78,7 @@ description: "Real world AI agent example tasks using Trigger.dev"
 
 ## Chat agents
 
-Build a durable, multi-turn chat agent with [`chat.agent()`](/ai-chat/overview). One run per conversation, with streaming, sessions and resumability handled for you.
+Build a durable, multi-turn chat agent with [`chat.agent()`](/ai-chat/overview). A durable session per conversation, with streaming and resumability handled for you.
 
 <CardGroup cols={2}>
   <Card

docs/guides/ai-agents/verify-news-article.mdx

@@ -68,6 +68,10 @@ export const extractClaims = task({
       const response = await generateText({
         model: anthropic("claude-sonnet-4-5"),
         messages,
+        experimental_telemetry: {
+          isEnabled: true,
+          functionId: "extract-claims",
+        },
       });
 
       const claims = response.text