Our conversational pathway agent keep misfiring the transition even though the condition are not met.
If your conversational pathway agent is transitioning before conditions are met, here are several solutions:
Review Transition Condition Structure
When examining your conversation flow, focus only on the transition_condition.prompt field—the condition field is not used by the LLM to trigger transitions(1). Ensure your prompt is concise, declarative, and matches actual language patterns the user would say(1).
Common Solutions
Make conditions more specific: Ensure transition conditions precisely match your intended triggers(2). For example, instead of a vague condition, use: “Only transition after the agent has asked if the user has anything else to add, and the user clearly says no”(3).
Add transition finetune examples: Provide examples to help the model understand your expectations(4)(2). This helps the LLM better recognize when conditions are actually met.
Adjust prompt engineering: Write conditions to be clear and not reference instructions too heavily(4). For instance, use “User said something about booking a meeting” rather than complex multi-part conditions(4).
Check equation conditions first: All equation conditions are evaluated before prompt conditions, from top to bottom(4). Make sure equation-based transitions aren’t firing unexpectedly.
Timing of Transitions
Transitions typically happen after the user speaks, though timing can vary based on node type(4). You can monitor which node is highlighted during testing to see exactly when transitions occur(4).
Cover All Cases
To prevent the agent from getting stuck, cover all possible transition scenarios in your conditions(4). Use global nodes for general cases like objection handling, then focus specific nodes on particular scenarios(4).
Hello @Alex_Krasun
Can you share the call id?
Thanks for getting back to me @Omar_Ashraf call_e79550572e8cb9a89e7e74a267a
The misfired transition is from (previous node: General Growth Areas) to (new node: Specific Learner-Requested Areas)
Looking forward to hearing from you
To debug the misfired transition from “General Growth Areas” to “Specific Learner-Requested Areas” for call call_e79550572e8cb9a89e7e74a267a, you should:
Check the Call Transcript
You can find node transitions inside the call transcript in the history tab(1). It will show the node names that it transitions from and to, which will help you identify exactly when the premature transition occurred(1).
Review Your Transition Condition
Focus on the transition_condition.prompt field for the edge connecting these two nodes(2). The condition string is not used by the LLM to trigger the transition(2). Make sure your prompt is:
Debugging Steps
Examine the condition specificity: Ensure transition conditions precisely match your intended triggers(3). Break down complex conditions into multiple simpler ones if needed(3).
Add transition finetune examples: Provide examples to help the model understand your expectations(1)(3). This is particularly helpful when you’ve observed an incorrect transition(1).
Check if equation conditions exist: All equation conditions are evaluated first before prompt conditions, from top to bottom(1). Verify that no equation-based transitions are firing unexpectedly.
Verify timing: Transitions usually happen after the user speaks, but can vary based on node type(1). During testing in the dashboard, watch which node is highlighted to see when the transition occurs(1).
Additional Recommendations
If the agent is transitioning before the user has fully responded to “General Growth Areas,” consider making the transition condition more explicit about what constitutes a complete response. For example, ensure the condition requires the user to have actually answered the questions in that node before moving forward.
Can you tell me why you think the transition is not met?
And I see you changed the transition condition, and this call is v94, and the current version is v97 with a different transition that might have caused a confusion.