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ZBrain's Zendesk Customer Query Resolution Agent automates ticket handling within Zendesk by delivering accurate, context-aware responses with minimal manual intervention. By leveraging a Large Language Model (LLM), it streamlines customer support workflows, accelerates resolution times, and ensures consistent, high-quality communication across all interactions.

Challenges the Zendesk Customer Query Resolution Agent Addresses:

Customer support teams using Zendesk often struggle with high ticket volumes, fragmented knowledge, and inconsistent responses. Teams spend excessive time navigating disconnected systems, leading to delayed resolutions and reduced customer satisfaction. Simple, repetitive queries consume valuable resources, while the lack of contextual understanding makes accurate triage difficult. Scaling support often leads to higher costs and inconsistent responses, putting customer trust and brand reputation at risk.

ZBrain's Zendesk Customer Query Resolution Agent intelligently reads new support tickets, extracts issue context, and queries internal knowledge bases to identify accurate answers. When a match is found, it generates personalized, structured responses and sends emails with accurate information. By automating ticket triage and response generation, the agent reduces manual workload, ensures timely resolutions, and improves overall support quality—empowering teams to scale efficiently while delivering exceptional customer service.

How does the Agent work?

The Zendesk Customer Query Resolution Agent automates customer support by efficiently managing queries and ensuring prompt, structured responses. Below is a step-by-step overview of its workflow:

Step 1: Ticket Detection & Data Extraction

The agent continuously monitors Zendesk for new or open tickets, ensuring no customer query is missed.

Key Tasks:

• Automatically detects and retrieves all open customer tickets.
• Implements a looping mechanism to handle multiple tickets efficiently.
• Extracts essential details, including the customer’s email, query content, and ticket metadata.

Outcome:

• Open tickets are identified and queued for further processing.
• Critical ticket details are captured for analysis.

Step 2: Knowledge Base Lookup

To provide accurate responses, the agent searches a centralized Knowledge Base (KB) for relevant information.

Key Tasks:

• Accesses a structured repository of FAQs and predefined answers.
• Searches for relevant responses based on the context of the customer’s query.

Outcome:

• The agent determines whether an appropriate answer is available in the KB.

Step 3: Intelligent Query Assessment & Response Decision

The LLM analyzes the query and available knowledge base data to generate a structured response.

Key Tasks:

• Evaluates if an answer is available:
  • If a relevant response is found, marks ‘answerpresent’ as ‘Yes’ and formats a structured email response in JSON (including recipient email, subject, and email body).
  • If no answer is available, sets ‘answerpresent’ to ‘No’, leaving all other fields empty.

Outcome:

• If a response exists, a structured email is generated.
• If no suitable answer is found, the query is flagged for further review.

Step 4: JSON Formatting & Workflow Optimization

To maintain consistency and efficiency, the extracted details are structured in a standardized format.

Key Tasks:

• Converts ticket data and response determinations into a JSON format using a JSON processor.
• Organizes query responses for seamless workflow integration.

Outcome:

• Queries and responses (if applicable) are formatted for streamlined execution.

Step 5: Automated Response & Unresolved Query Handling

The agent ensures timely customer communication and logs unresolved queries for future improvements.

Key Tasks:

• Sends an automated email response via Gmail for resolvable queries, with the LLM generating context-aware email content based on the identified response.
  • Ensures all replies align with predefined messaging standards.
• If no suitable KB response is found:
  • Logs the unresolved query for future reference.
  • Attaches the ticket link with a response message stating: "Unable to find a relevant response to the customer’s query in the knowledge base."
  • Supports ongoing KB updates to improve future accuracy.

Outcome:

• Customers receive prompt and precise responses.
• Unresolved queries are recorded to enhance the KB.

Step 6: Reporting & Escalation

The agent maintains transparency by tracking all interactions and escalating complex cases for manual review.

Key Tasks:

• Generates a comprehensive report of all open tickets, including:
  • Ticket link, email status, and complete email content.
  • If no response is sent, the ticket is attached with a note for reference.
• Evaluates whether escalation is required based on predefined criteria.
• If manual intervention is needed:
  • Creates a support ticket in the customer success channel.
  • Summarizes missing details requiring follow-up.
  • Suggests relevant questions for human agents to ask customers.

Outcome:

• A detailed record of all tickets is maintained.
• Complex queries are escalated for human resolution, ensuring customer satisfaction.

Why use the Zendesk Customer Query Resolution Agent?

• Faster Response Time: Automates query handling, reducing response time significantly.
• Increased Accuracy: Uses LLM-powered analysis for precise, context-aware replies.
• Seamless Workflow: Extracts, processes, and resolves tickets systematically.
• Reduced Manual Effort: Minimizes human intervention by automating common query resolution.
• Scalable Query Management: Efficiently handles multiple queries simultaneously.
• Effective Escalation: Flags complex issues for human intervention when needed.
• Seamless Zendesk Integration: Works natively within Zendesk, streamlining query resolution without disrupting existing workflows.
• Improved Customer Experience: Delivers timely, clear, and accurate resolutions, enhancing brand perception.

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The agent integrates seamlessly with existing enterprise systems, allowing it to pull relevant data from past tickets, logs, and knowledge bases to inform its suggestions. By leveraging generative AI, it identifies patterns and insights from historical data, enabling it to provide tailored recommendations that are specific to each unique ticket. This capability helps support agents navigate complex or unfamiliar issues with confidence, minimizing the risk of errors and ensuring that customers receive timely and accurate resolutions. The agent’s ability to adapt to different ticket types and scenarios makes it a versatile tool for customer service teams, regardless of the complexity or volume of tickets they handle.

A key feature of the Next Step Suggestion Agent is its human feedback loop, which allows users to provide input in natural language. This feedback is used to refine and improve the agent’s recommendations over time, ensuring that it continuously evolves to meet the needs of the team. By incorporating user insights, the agent becomes more aligned with the specific workflows and preferences of the organization, further enhancing its effectiveness. This iterative improvement process, combined with its real-time suggestion capabilities, makes the agent a valuable asset for customer service departments aiming to optimize their ticket resolution processes and deliver a higher standard of support to their customers.

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Customer Service

Zendesk Customer Query Resolution Agent

Automates customer support by retrieving open tickets, searching the knowledge base, sending email responses, and logging unresolved queries for future reference.

Customer Service

Next Step Suggestion Agent

Provides recommended next steps for each support ticket based on ticket type, history, and predefined resolution procedures.