How to configure Zendesk AI agent fallback messages: A complete guide

When a customer asks your AI agent something it can't answer, what happens next? That moment, the fallback response, can make or break the customer experience. Get it right, and customers feel supported even when the AI falls short. Get it wrong, and you've got frustrated customers bouncing between unhelpful automated responses.

This guide walks you through everything you need to know about configuring fallback messages in Zendesk AI agents. You'll learn how to set them up, what makes them effective, and how to avoid common pitfalls that leave customers stuck.

What is a Zendesk AI agent fallback message?

A fallback message is the response your AI agent sends when it cannot understand a customer's question or doesn't have the information to provide a useful answer. Think of it as the AI's way of saying "I don't know, but here's what I can do instead."

In Zendesk, fallback messages trigger in a few specific scenarios:

• The customer's question falls outside the AI's trained knowledge
• The AI cannot confidently determine the customer's intent
• The query requires information from systems the AI cannot access
• The question is too ambiguous to route properly

The default fallback message in Zendesk is: "Sorry, I can't answer that. Here are some topics that might help though." It's functional, but generic. Most teams quickly realize they need something more aligned with their brand voice and customer needs.

Here's the short version: your fallback message is a safety net. It should acknowledge the limitation, redirect the customer toward a solution, and maintain the conversational flow. A poorly worded fallback feels like hitting a dead end. A well-crafted one feels like a helpful detour.

How to configure fallback messages in Zendesk

Setting up your fallback message happens in the Zendesk Admin Center. The process is straightforward, but the location varies slightly depending on whether you're using essential AI agents or the advanced AI agents add-on.

Step 1: Access AI agent settings

Start by navigating to the Admin Center. Click AI in the left sidebar, then select AI agents. If you have multiple AI agents configured, select the one you want to edit.

You'll need administrator permissions to make these changes. If you don't see the AI section in your Admin Center, check that your account has the right role assigned.

Step 2: Locate messaging behavior settings

Once you're in the AI agent configuration, look for the Messaging behavior tab. This is where you control how your AI agent responds in different scenarios.

Scroll down to the section labeled "If the AI agent can't understand a question." This is your fallback configuration area. You'll see a text field containing your current fallback message, along with options to add suggested answers that appear as quick-reply buttons.

Step 3: Customize your fallback message

Now comes the important part: writing the actual message. Replace the default text with something that fits your brand voice and guides customers toward a resolution.

You can also add up to 10 pre-created answers as suggested options. These appear as buttons customers can click, giving them a clear path forward. Good options include "Talk to a human," "Browse help center," or "Start over."

Click Save when you're done. The changes take effect immediately for new conversations.

Best practices for writing effective fallback messages

The words you choose in your fallback message matter more than you might think. A vague "I don't understand" leaves customers frustrated. A clear, helpful message keeps them engaged.

Here are the key principles:

Acknowledge the limitation clearly but empathetically. Don't pretend the AI understood when it didn't. Something like "I don't have a specific answer for that" is honest without sounding incompetent.

Offer specific next steps. Every fallback should give the customer something to do next. Options include speaking to a human, browsing the help center, rephrasing their question, or checking back later.

Match your brand voice. If your company is casual and friendly, your fallback should be too. If you're more formal, keep it professional. The fallback is still part of your brand experience.

Keep it concise. Customers who hit a fallback are already having a suboptimal experience. Don't make them read a paragraph. Two to three sentences is usually enough.

Include escalation options. Make it easy for customers to reach a human when the AI can't help. A clearly labeled "Talk to a human" button reduces friction and frustration.

Test across different scenarios. Try your fallback with various edge cases. Does it make sense for billing questions? Technical issues? Account problems? One size rarely fits all, but your message should handle the most common situations gracefully.

Zendesk AI agent fallback message examples by use case

Sometimes the best way to understand what works is to see real examples. Here are fallback messages tailored to different industries and use cases.

E-commerce fallback example

"I'm not sure I can help with that specific question. For order issues, you can [check your order status] or [chat with our team]. For product questions, try browsing our [help center]."

This works because it acknowledges the limitation while immediately offering relevant next steps. The links direct customers to the most common resolution paths for e-commerce: order tracking and human support.

SaaS support fallback example

"I don't have a specific answer for that. You can [search our documentation], [submit a support ticket], or [schedule a call] with our technical team. If this is urgent, I can connect you with someone now."

SaaS customers often have technical questions that require detailed answers. This fallback offers self-service options for those who want them and escalation for those who need immediate help.

General customer service fallback example

"I want to make sure you get the right help. Could you rephrase your question, or would you prefer to [speak with a specialist]? Our team is available [hours] and usually responds within [timeframe]."

This approach works well for general inquiries because it gives the customer an easy out (rephrasing) while setting clear expectations about human support availability.

Common Zendesk AI agent fallback issues and how to fix them

Even with careful configuration, you might run into issues. Here are the most common problems and how to address them.

Issue: Fallback triggers too often

If your AI agent is falling back on questions it should be able to answer, check your knowledge sources. The AI can only answer based on what it has been trained on. Make sure your help center articles cover the topics customers are asking about.

You might also need to adjust your confidence threshold. If the AI is too cautious, it falls back even when it has a reasonable answer. This setting is found in the AI agent's advanced configuration.

Issue: Customers get stuck in fallback loops

Sometimes customers rephrase the same question multiple times, hitting the fallback each time. To prevent this, add a counter that tracks fallback triggers within a conversation. After two or three fallbacks, automatically offer to transfer to a human.

Issue: Can't customize normal AI responses

This is a known limitation in Zendesk. The platform allows customization of fallback messages but not the normal AI-generated responses. Users have requested this feature in the Zendesk community, but as of now, you can only customize the fallback.

One workaround is to use the AI agent's instructions to guide how it formats responses, though this is less precise than direct template control.

Issue: Ticket status doesn't change based on response type

When the AI sends a fallback, you might want the ticket to stay open for human review. When it sends a successful AI response, you might want it set to pending. Zendesk doesn't provide direct conditional logic for this, but you can use tags and triggers.

Zendesk adds tags like ar_suggest_true (AI answered) and ar_suggest_false (fallback sent) to tickets. Create triggers that check for these tags and adjust ticket status accordingly. For example, a trigger could set tickets with ar_suggest_false to open status so agents know to review them.

How eesel AI handles fallback differently

While Zendesk provides basic fallback functionality, there's a fundamentally different approach worth considering. At eesel AI, we don't draw a hard line between "AI responses" and "fallback messages." Instead, we treat every interaction as an opportunity to learn and improve.

Here's how our approach differs:

No rigid fallback vs. normal response distinction. Rather than having a separate fallback message that triggers when the AI is uncertain, eesel AI drafts responses for every query based on your knowledge base, past tickets, and macros. If it's not confident, the response goes to an agent for review rather than sending a generic fallback to the customer.

Continuous learning from agent corrections. When an agent edits an AI-drafted response, eesel AI learns from that correction. Over time, this reduces the need for fallbacks because the AI gets better at handling edge cases it previously struggled with.

Plain-English escalation rules. Instead of complex trigger configurations, you define escalation logic in natural language. For example: "If the refund request is over 30 days, escalate to a human" or "Always escalate billing disputes to the finance team."

No API timeout constraints. Zendesk has a fixed API timeout that cannot be increased, which limits complex integrations. eesel AI operates outside these constraints, allowing for deeper integrations with your existing systems.

If you're finding Zendesk's fallback limitations frustrating, you can try eesel AI alongside your existing setup. Our AI Copilot drafts responses for agent review, and once you're confident in its performance, you can level up to full autonomous ticket resolution.

Testing and optimizing your Zendesk AI agent fallback messages

Configuration isn't a one-time task. To keep your fallback messages effective, you need to monitor and refine them over time.

Run simulations on past tickets. Before deploying changes, test your fallback message against historical conversations. Zendesk's AI agent insights can show you how often fallback triggers and what customers do next.

Monitor fallback trigger rates. A sudden spike in fallback rates usually indicates a problem. Maybe a product launch introduced new questions the AI hasn't learned yet, or a website change broke a common query path. Track this metric weekly.

A/B test different message variations. If you have enough volume, test two different fallback messages against each other. Measure which one leads to higher customer satisfaction and lower escalation rates.

Track escalation rates from fallback. The goal of a fallback message is to guide customers to a resolution. If customers consistently escalate after seeing your fallback, the message isn't doing its job.

Iterate based on customer feedback. Read the tickets that follow fallback messages. What are customers actually asking? Use this insight to improve your knowledge base and refine your fallback wording.

Getting started with better AI fallback handling

Your fallback message is a small but critical piece of the customer experience. In Zendesk, you can configure it through the AI agent settings by navigating to Admin Center > AI > AI agents > Messaging behavior. The key is writing something that acknowledges the limitation while guiding customers toward a solution.

If you're hitting the limitations of Zendesk's native AI agents, whether it's the inability to customize normal responses, the fixed API timeouts, or the rigid fallback structure, consider exploring alternatives. eesel AI integrates directly with Zendesk and offers a more flexible approach to AI-powered support.

You can try eesel AI free and see how it handles the scenarios that have been challenging in your current setup. Or book a demo to see the platform in action.