Maintenance and Updates › Responding to User Feedback
AI Agent HandbookAll chapters Maintenance and Updates › Responding to User FeedbackOne section · Sections in this chapter8 / 19 ↓
Your users are your best source of information about what needs to improve. They'll tell you what works, what doesn't, and what they wish the agent could do.
Collect Feedback Systematically
Make it easy for users to give feedback. This might be as simple as a feedback button in your interface, or a contact email. Some agents include a feedback mechanism right in the conversation:
User: What's the weather in Seattle?
Agent: The weather in Seattle is currently 65°F and partly cloudy.
Was this response helpful? [Yes] [No] [Give feedback]Track this feedback in a simple system. A spreadsheet works fine for small-scale agents. Larger systems might use a ticketing system or database.
Categorize and Prioritize
As feedback comes in, categorize it:
- Bugs: Things that are broken and need fixing
- Improvements: Things that work but could be better
- Feature requests: New capabilities users want
- Confusion: Areas where users don't understand how to use the agent
Prioritize based on:
- Impact: How many users does this affect?
- Severity: How much does it hurt the user experience?
- Effort: How hard is it to fix?
Fix high-impact, low-effort issues first. These give you the best return on your maintenance time.
Close the Loop
When you fix something based on user feedback, let the users know. This shows you're listening and encourages more feedback:
"Thanks for reporting that the weather tool wasn't working in Canada. We've fixed this issue and it should work correctly now."
Learn from Patterns
Sometimes individual feedback items seem random, but patterns emerge when you look at many reports. If five users independently mention that the agent is slow in the afternoon, investigate. Maybe there's a performance issue during peak hours.
If multiple users ask for the same new capability, that's a strong signal it's worth building.