Hi Everyone,
We are looking to formalize a continuous improvement process for our Virtual Agent performance by establishing a clear, end-to-end workflow based on Virtual Agent Feedback Tasks (generated from negative user feedback/surveys).
Currently, feedback tasks are generated, but we want to build an actionable operational model around them. I’d love to hear how other organizations handle this.
Specifically:
Analysis Process: What is your standard procedure for reviewing transcripts and root-causing issues (e.g., NLU intent mismatch, poor KB article formatting, missing topic, or AI search gap)?
Actionable Remediation: Once a issue is identified, what downstream workflows do you trigger? (e.g., updating AI Search profiles, drafting new KB articles, tuning topic prompts, or raising a formal Virtual Agent Improvement Request?)
Governance & Automation: Have you created custom catalog items, automated routing rules, or dashboards to track the lifecycle of feedback remediation?
If you have implemented a structured workflow or best practices around this, I’d appreciate any insights, governance tips, or lessons learned from your projects!
Thanks in advance!
