support chat flow improvements

Author: Vlad Velici

src/pages/docs/guides/chat/support-chat.mdx

@@ -62,7 +62,7 @@ A typical support chat system follows this flow:
 
 ### Pub/Sub channels and Chat rooms <a id="channels-and-rooms"/>
 
-Use both Ably Pub/Sub and the Ably Chat SDK to build a support chat system. The high-level architecture is as follows:
+Ably Chat handles the messaging experience within a ticket, powered by a single chat room, while Ably Pub/Sub coordinates system-wide events like new ticket notifications and agent routing. Use both to build a complete support chat system. The high-level architecture is as follows:
 
 - Use a dedicated chat room for each ticket, for example `support-chat:ticket-342`.
 - Use one or more Pub/Sub channels to notify agents of new tickets and chats requiring attention:
@@ -101,6 +101,8 @@ After the chat has started, you can provide a rich chat experience with the Ably
 
 ### Authentication <a id="customer-authentication"/>
 
+Customers need access only to their own support tickets, not to other customers' chat rooms. A room naming convention that encodes ownership makes this straightforward to enforce.
+
 Name each support chat room uniquely by using the user ID as a prefix followed by the ticket ID. For example, `support:user-343:ticket-992`.
 
 This approach allows you to grant permission for users to access all their own support tickets, even before they are created, making authentication and access control straightforward. Grant each customer a JWT with access to their own support tickets by using a capability resource specifier such as `support:user-343:*`.
@@ -236,7 +238,7 @@ Presence can also be used to share data about the current user to the support ag
 
 ## Assigning tickets to agents
 
-A key part of a support system is assigning tickets to agents. You can do this on your server either in advance as tickets are created, by tracking agent workload, or by broadcasting all tickets to all agents in a shared queue.
+With the customer-facing widget handling message delivery and presence, the next challenge is connecting those conversations to agents. A key part of a support system is deciding how tickets reach the right agent at the right time. You can either do this on your server as tickets are created by tracking agent workload, or by broadcasting all tickets to all agents in a shared queue.
 
 How do agents know about new tickets without being attached to every room?
 
@@ -330,6 +332,8 @@ When the agents dashboard receives this message, it removes the ticket from the
 
 Instead of using a channel per agent, use a channel per team or department, for example `support:sales` or `support:engineering`. Follow the same pattern as the server-side ticket assignment to decide which channel to use for each ticket, then apply the shared inbox queue pattern for tickets that reach a team or department.
 
+This hybrid approach works well for organizations with specialized teams: route tickets to the appropriate department first, then let agents within that team claim them from the shared queue.
+
 ## Agent dashboard <a id="agent-dashboard"/>
 
 The agent dashboard is where support chat demands careful architectural thinking. Agents need to see and respond to multiple tickets, know when new tickets need attention, coordinate with other agents, and access context and AI assistance.
@@ -383,7 +387,7 @@ You need to signal to the integrations when the ticket is no longer handled by A
 
 ### Example architecture for aiding support agents with AI
 
-AI can assist agents in several ways:
+Beyond handling customer conversations directly, AI can work alongside human agents to make them more effective. AI can assist agents in several ways:
 
 - Drafting responses for agents.
 - Searching for similar tickets and how they were resolved.
@@ -396,7 +400,7 @@ Implement this client-side in the agent dashboard, where each message from the c
 
 ## Ticket resolution and archiving <a id="ticket-resolution"/>
 
-When a ticket is resolved, update its status in your system, optionally collect customer feedback, and export the chat history for long-term storage.
+Whether resolved by AI or a human agent, every ticket eventually reaches its conclusion. When a ticket is resolved, update its status in your system, optionally collect customer feedback, and export the chat history for long-term storage.
 
 **Collecting feedback:** A straightforward approach is to send a feedback request message when the agent marks the ticket resolved. The customer responds with a rating (for example, emojis or stars), which your server captures and associates with the ticket.
 
@@ -415,7 +419,7 @@ For more sophisticated export patterns such as continuous ingestion and streamin
 
 ## Production-ready checklist
 
-Before you go live with your support chat, review these key points:
+The patterns in this guide handle the core architecture. Before you go live with your support chat, review these key points to ensure your implementation is production-ready:
 
 * **Authentication strategy:** Ensure you're using token authentication for all client-side communication with appropriate capabilities for each participant. Make sure capabilities are configured correctly for each type of participant (agents and customers).
 * **Scale planning:** Confirm you are on the right Ably package for your expected volume of concurrent tickets.