Update README.md

README.md

@@ -26,7 +26,7 @@ The agent can be configured to pursue different goals using the `AGENT_GOAL` env
 
 #### Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost
 - `AGENT_GOAL=goal_match_train_invoice` - Focuses on Premier League match attendance with train booking and invoice generation
-    - This is a new goal that is part of an upcoming conference talk
+    - This goal was part of [Temporal's Replay 2025 conference keynote demo](https://www.youtube.com/watch?v=YDxAWrIBQNE)
 
 If not specified, the agent defaults to `goal_event_flight_invoice`. Each goal comes with its own set of tools and conversation flows designed for specific use cases. You can examine `tools/goal_registry.py` to see the detailed configuration of each goal.
 
@@ -189,4 +189,4 @@ If you're running your train API above on a different host/port then change the
 - In a prod setting, I would need to ensure that payload data is stored separately (e.g. in S3 or a noSQL db - the claim-check pattern), or otherwise 'garbage collected'. Without these techniques, long conversations will fill up the workflow's conversation history, and start to breach Temporal event history payload limits.
 - Continue-as-new shouldn't be a big consideration for this use case (as it would take many conversational turns to trigger). Regardless, I should ensure that it's able to carry the agent state over to the new workflow execution.
 - Perhaps the UI should show when the LLM response is being retried (i.e. activity retry attempt because the LLM provided bad output)
-- Tests would be nice!
+- Tests would be nice!