Hello! In multiple calls I’ve been having issues with the agent not transitioning nodes properly even though all the requirements for the transition have been met, and it stays stuck on that node. For example, in the call provided above, the agent was collecting the callers information to pass along to the estimate team of this businesses. Once it collected that information, it was supposed to move to the confirmation node where the agent reads back the callers info to confirm it got everything correct, then end the call. But as you can see the node never transitioned out of collecting the callers information.
It seems that conversational flow agents in rigid mode have issues with node transitions, because I also have issues with the agent knowing when to transfer a call or not. It’s quite upsetting. Would an agent like this be better suited for a single prompt agent, since there are only 3-5 pathways a caller usually goes?
Regardless, I need these node issues fixed. Any help?
Based on the information in the sources, here are specific steps to address your node transition issues in Conversation Flow:
For Node Transition Problems:
When node transitions aren’t working as expected, you should:(1)
Review your transition conditions to ensure they precisely match your intended triggers(1)
Add transition finetune examples to help the model understand your expectations(1)
Add more nodes to cover edge cases, particularly global nodes for handling unexpected situations(1)
Make transition conditions more flexible and general(1)
For Agent Not Following Instructions Within a Node:
If the agent isn’t collecting information properly before transitioning:(1)
Split the node into multiple nodes - For example, if a node collects customer name, phone number, and address, the agent might inconsistently ask for only some information. You can improve consistency by splitting this into separate nodes(1)
Change the node model - If instructions are concise but the agent struggles to follow them, try using a more capable LLM model for this node(1)
Add conversation finetune examples to achieve a specific response style(1)
Adjust the LLM temperature if the agent’s responses are inconsistent(1)
Regarding Your Question About Single vs. Multi-Prompt:
For scenarios with only 3-5 pathways, the sources suggest that starting with a single prompt is often recommended until you need the additional complexity of multi-prompt or conversation flow(2). Single prompt is straightforward and ideal for simple use cases where quick responses are needed(3). However, conversation flow provides more fine-grained control over dialogue paths and is beneficial when you need structured conversations with predictable outcomes(4).
I’d recommend implementing the debugging steps above first, particularly adding transition finetune examples and reviewing your transition conditions, before switching agent types.
Based on the sources, here’s how to create a finetune example for your transition issue:
For Transition Finetune Examples:
When configuring a finetune example for transitions, you need to provide both a transcript as context and the transition result(1).
For your specific case where the agent should transition from collecting caller information to the confirmation node, you would:
Provide a transcript as context - You can select user, agent, or function as the role of the transcript(1). You don’t need to provide the entire call transcript, just the relevant part(1).
Specify the transition result - Indicate which node the agent should transition to(1).
Example Structure for Your Use Case:
Since your agent is stuck in the information collection node and not transitioning to confirmation, your finetune example might look like:
The sources note that if you cannot distinguish between different nodes available as transition targets, you can try renaming your nodes to make them easier to distinguish(1).
Important Note About Conversation Finetune Examples:
For conversation nodes specifically, at least one agent response is required when supplying a transcript as context, as this finetunes the agent’s response(1).
To access the finetune examples feature, you can find it in the node settings for Conversation Nodes and Function Nodes(2).