Stop Wasting Agents on Low-Value Tickets with Gemini AI Triage

Implement Intent Classification to Auto-Route Low-Value Tickets

The foundation of AI-powered triage is robust intent detection. Configure Gemini to read incoming ticket text (subject, body, and basic metadata) and classify it into a standardised set of intents such as “password reset”, “order status”, “address change”, “faq-product-info”, or “billing-issue”. These intents then drive routing rules.

Start with a simple classification prompt that enforces a strict schema and can be integrated into your ticketing or middleware system.

System: You are an assistant that classifies customer support tickets.
Output ONLY a JSON object with fields: intent, confidence (0-1), sensitive (true/false).

User ticket:
{{ticket_subject}}
{{ticket_body}}

Valid intents:
- password_reset
- order_status
- change_address
- faq_product_info
- technical_issue
- billing_issue
- other

Describe the main intent using one of the valid intents.

Use the intent and confidence scores to decide whether the ticket should be auto-answered, guided to self-service, or routed to an agent. Low-confidence classifications should always fall back to a human.

Deploy a Gemini-Powered Assistant in Front of the Ticket Form

One of the fastest ways to deflect low-value tickets is to intercept them before they are submitted. Embed a Gemini-based conversational assistant directly into your web and mobile contact flows. The assistant’s job: understand the request, offer relevant knowledge base articles, and guide users through simple workflows (e.g., resetting a password or tracking an order) without creating a ticket.

Use a prompt that enforces containment where safe and gracefully escalates when necessary.

System: You are a customer self-service assistant.
Goal: Resolve the issue without creating a support ticket IF it is about:
- password reset
- order status
- address change
- generic FAQs
If the issue is complex, clearly tell the user you are handing them to a human agent.
Ask targeted questions only when needed. Keep answers short and actionable.

Instrument the assistant with events ("resolved", "escalated", "requested_agent") so you can track how many interactions are successfully contained versus escalated.

Auto-Suggest Knowledge Base Articles and First Responses for Agents

Not every low-value ticket can be fully deflected, but many can be accelerated. Integrate Gemini into your agent desktop so that each incoming ticket is enriched with suggested knowledge base articles and a drafted first response that the agent can quickly review and send.

Configure Gemini to search and reason over your KB content and generate a concise, brand-aligned reply.

System: You are a customer support assistant.
You receive a ticket and relevant knowledge base articles.
Write a concise, friendly reply that:
- directly answers the question
- includes clear steps or links
- avoids speculation
If no relevant article exists, ask 1-2 clarifying questions.

Ticket:
{{ticket_text}}

Relevant articles:
{{kb_snippets}}

Measure the impact on average handle time (AHT) and first response time for these low-complexity tickets. Over time, you can move certain intents from “AI-assisted agent” to “full self-service”.

Use Gemini to Summarise and Triage Email Backlogs

Email channels often hide a long tail of low-value requests. Connect Gemini to your support inbox (via your helpdesk or a middleware) to batch-process incoming messages: summarise, classify, and propose actions. For obvious low-value intents (e.g., order status), the system can automatically send a response template enriched with dynamic data (like order link or tracking information) after an optional human check.

Design a backend workflow where each email is passed to Gemini for a structured summary.

System: Summarise this customer email in 2 sentences.
Then output structured fields: intent, urgency (low/medium/high),
requires_human (true/false), suggested_reply.

Email:
{{email_body}}

Use the requires_human flag to filter which tickets an agent must see, and auto-send the suggested_reply for low-risk cases once an agent has quickly skimmed and approved it.

Connect Gemini with Operational Systems for Real Self-Service

To truly deflect tickets, Gemini needs more than text; it needs access to operational data such as orders, accounts, and subscriptions. Work with your engineering team to expose read-only APIs (e.g., get_order_status, list_recent_orders, get_account_state) that Gemini can call via a controlled orchestration layer.

Instead of letting the model hallucinate, define deterministic “tools” it may use and instruct it via the prompt.

System: You can use the following tools:
- get_order_status(order_id)
- list_recent_orders(customer_id)
Use tools ONLY when needed to answer the question.
If tool data is missing, say you cannot access it and offer to connect to an agent.
Never guess order IDs.

User:
"Where is my last order?"

This pattern lets Gemini handle a large share of status and account questions with live data, while still escalating neatly when information is incomplete or risky.

Track KPIs and Run Controlled Experiments on Deflection

Once Gemini-powered triage is live, move into measurement mode. Define a minimal KPI set for ticket deflection and AI performance: deflection rate (percentage of interactions resolved without ticket creation), containment rate (AI conversations not escalated), AHT for low-value tickets, CSAT for AI-assisted and AI-only interactions, and agent satisfaction.

Run A/B tests for critical flows, such as different wording in the self-service assistant or stricter vs. looser escalation thresholds. For instance, you can test whether asking one clarifying question before escalation increases containment without harming CSAT. Review transcripts regularly with your support leads and refine prompts and routing rules based on concrete examples.

With well-designed workflows, most organisations can realistically expect a 20–40% reduction in low-value ticket volume over several months, alongside faster first responses and more focused agent time on complex issues.