Recommendations
Every open fix across your fleet, ranked by impact. Each one links back to the call that raised it.
High impact
2Ensure booking and contact update tool actions are triggered post-confirmation
The booking flow completed verbally but no tool actions (updateContactField, booking router) were fired. This means no data was persisted and the appointment may not actually be recorded in the system.
Add a conditional flow node immediately after the caller confirms appointment details that explicitly triggers the booking tool and updateContactField action with all collected fields (name, phone, appointment time, patient type, insurance). Validate that these nodes fire before end_call is executed.
Fix insurance provider extraction logic to capture multi-word values
Bella extracted only the second word 'Dental' instead of the full provider name 'Delta Dental'. This is likely due to a prompt or parsing issue that truncates or misreads multi-word entity values.
Update the prompt or entity extraction step to capture the full insurance provider name as a contiguous phrase, and instruct Bella to repeat the full name back verbatim for confirmation. Add a few-shot example: caller says 'Delta Dental' → agent confirms 'Delta Dental'.
Medium impact
1Fix response Concatenation/TTS rendering bug causing garbled closings
The closing at [14] contains a malformed string ('to seeing you!IfThank you!') indicating two response segments were merged without a separator or newline. This likely stems from a template or variable injection error.
Audit the closing response template for improper string concatenation. Ensure conditional phrases (e.g., 'If you have any questions...') are separated from prior sentences with proper punctuation and spacing. Add an integration test that validates the final spoken output for well-formedness.
Low impact
1Train agent to acknowledge unclear caller phrasing before inferring intent
When a caller uses garbled or ambiguous language, a brief acknowledgment before reframing the question improves naturalness and trust.
Add training examples where the agent says something like 'It sounds like you may be looking to book an appointment — is that right?' rather than immediately pivoting to a confirmation question, to make the interaction feel more human and empathetic.