When users browse an online shop, hesitation often reveals itself through behavior, not words. A customer looking at shoes, then switching to skateboards, then back to shoes. That back and forth signals uncertainty. Wouldn't it be great if your chatbot could detect this pattern and respond intelligently?
That's exactly what the Context Change Detection flow demonstrates. It's a practical example of what the underlying technology Reactive Graph Sequencing (RGS) makes possible without writing a single line of custom code.
The flow is built around one of Wanderer's core mechanics: you can always jump back in a conversation and correct an answer. This isn't a workaround or an edge case. In RGS, retroactive re-sequencing is the default. When a user changes a decision, the graph re-traverses and generates a new sequence based on the updated state.
But here's where it gets interesting. You can detect these context changes and react to them.
Each product sub-branch in the flow contains a count node. A count node does exactly what the name suggests: it counts how often it has been activated within the current context. Outgoing edges can then evaluate this count and enable alternative routes through the graph.
The flow uses this counting mechanism to gradually escalate its responses:
First visit: The chatbot responds normally.
I'd be happy to help you with shoes.
After the first switch back: The message subtly changes to acknowledge the return.
Of course I'll help you with shoes again.
After multiple switches between branches: The flow recognizes a pattern of uncertainty and offers personal assistance.
You have switched between shoes and skateboards several times. Would you prefer to receive personal advice?
Each escalation level is simply an alternative edge route, activated by the count node reaching a specific threshold. No scripting, no external logic. Just the graph doing what it does best.