The handoff from chatbot to human agent is where most AI implementations fail. Without proper configuration, customers repeat themselves, agents lack context, and satisfaction drops.
This guide covers how to configure seamless handoffs that maintain context and keep customers happy.
The Two Handoff Problems
When chatbots transfer to humans, two issues typically occur:
1. Context Loss Agents see a name and email but nothing about what the customer asked, what the bot already tried, or why the handoff happened.
2. Broken Expectations Customers don't know how long they'll wait, whether they need to repeat themselves, or if anyone is actually coming.
Both problems are preventable with proper configuration.
Handoff Trigger Configuration
Set up four trigger types for comprehensive coverage:
| Trigger Type | When It Fires | Why It Matters |
|---|---|---|
| Explicit request | Customer says "speak to human," "agent," etc. | Customer asked—give them one |
| Sentiment detection | Frustration or anger detected | Catches customers who need help but won't ask |
| Topic match | Billing disputes, refunds, legal, security | Some topics always need humans |
| Confidence threshold | AI confidence below 70% | Don't guess when uncertain |
Topic-Based Routing
Configure specific topics to automatically route to humans:
- Billing disputes
- Refund requests over $X amount
- Legal questions
- Account security issues
- Complex technical troubleshooting
- Escalation requests
The Handoff Message
What customers see when handoff triggers:
This question needs a human touch. Let me connect you with our team.
Quick heads-up:
- Current wait: About [X] minutes
- I'm sharing our conversation so you won't need to repeat yourself
- Our team member will have all the context
If you'd rather not wait, I can have someone email you within 2 hours.
Which works better?
This message accomplishes three things:
- Sets expectations — Accurate wait time
- Promises no repetition — Context transfer confirmed
- Offers alternatives — Email option for busy customers
Context Delivery to Agents
What agents should see when picking up a handoff:
HANDOFF SUMMARY
---------------
Customer: [email]
Current page: /pricing
Time in chat: 4 minutes
ORIGINAL QUESTION:
"I was charged twice for my subscription last month"
BOT ATTEMPTS:
- Showed refund policy
- Explained billing cycle
- Customer asked about specific charge
SUGGESTED ACTION:
Check account for duplicate charges
Process refund if confirmed
SENTIMENT: Frustrated (escalating)
With this context, agents can skip "How can I help you?" and go straight to solving the problem.
Notification Configuration
Set up notifications based on urgency:
Urgent handoffs (billing, frustrated customers):
- Instant Slack/Teams notification with summary
- Email notification with full transcript
- Dashboard alert for any available agent
Non-urgent handoffs:
- Email notification only
- Added to queue for next available agent
After-Hours Handoffs
When humans aren't available, configure appropriate responses:
Our team isn't available right now (we're online 9am-6pm EST).
I've captured everything from our conversation—someone will reach out by 10am tomorrow.
Want me to:
1. Email you when we respond
2. Have someone call you
3. Just reply here—I'll save the message
After-hours configuration:
- Full conversation saved to handoff queue
- Email notification to on-call person for emergencies
- Customer gets follow-up at start of business
Measuring Handoff Quality
Track these metrics to optimize handoff performance:
Handoff Rate
| Rate | What It Indicates |
|---|---|
| Under 10% | Suspiciously low—may be frustrating customers |
| 10-20% | Healthy balance—AI handles most, humans handle complex |
| 20-30% | Acceptable—look for patterns in escalations |
| Over 30% | AI not effective—needs better training |
Response Time
Time from handoff to first human message:
- Under 5 minutes: Excellent
- 5-15 minutes: Acceptable
- Over 15 minutes: Customers are bouncing
Post-Handoff Satisfaction
Compare ratings between:
- AI-only conversations
- Human-handled conversations
- Conversations with handoffs
If handoff conversations rate lower than AI-only, the transition is creating friction.
Common Handoff Mistakes
Premature handoff Bot transfers too quickly, wasting human time on questions AI could handle.
Fix: Increase confidence thresholds. Let the bot try harder before escalating.
Context black hole Agent knows nothing about prior conversation.
Fix: Automatic context passing. This should never be manual.
Cold restart Agent starts with "How can I help you?"
Fix: Agent's first message should acknowledge what's been discussed.
Queue dishonesty "Someone will be right with you" when wait is 20 minutes.
Fix: Honest wait times and alternatives for long waits.
Repeat question detection Customer asks the same thing in different ways.
Fix: If someone asks "do you have a free trial?" then "can I try it before paying?"—they're asking the same thing. If the bot didn't answer, escalate.
Handoff Threshold Framework
| Trigger | Threshold | Rationale |
|---|---|---|
| Explicit request | Always | Customer asked for human |
| Topic match | Specific topics | Some things always need humans |
| Sentiment | Negative detected twice | One negative comment may be normal; two means escalating frustration |
| Confidence | Below 70% | Don't guess when uncertain |
| Repeat question | Same question asked 2+ ways | Customer isn't getting a real answer |
Getting Started
Start free with Kya to configure seamless handoffs:
- Go to Settings → Human Handoff
- Configure handoff triggers (explicit, sentiment, topic, confidence)
- Set up handoff messages with wait times and alternatives
- Configure notification methods (email, Slack, dashboard)
- Set up after-hours behavior
The first time an agent picks up a chat with full context, they'll see the difference immediately.
Handoffs are the moment of truth for chatbot implementations.
Get them right, and customers feel supported even when AI can't help alone.
Get them wrong, and you undo all the good work the bot did.
Context. Expectations. Speed.
Nail those three, and you've mastered the hardest part of chatbot support.
