From Ticket Queue to Agentic Triage: AI-Assisted Support for a Growing Service Desk
The Challenge
A service-desk operation was scaling headcount linearly with ticket volume, with first-line staff spending most of their time on classification and routine resolution rather than genuinely hard problems. Manual triage was consuming disproportionate effort, leading to slower response times and inefficient use of skilled personnel.
The Approach
We designed and deployed an agentic triage layer—LLM-driven classification, knowledge retrieval, and draft resolution with human sign-off—built around the client's existing tooling rather than a rip-and-replace. The goal was to augment, not replace, human judgment while reducing repetitive cognitive load.
System Architecture
Key Components
- Input Normalizer: Standardizes incoming tickets from various channels (email, portal, API) into a common format.
- Classification Agent: Uses an LLM (via API) with retrieval augmentation to categorize tickets by type, priority, and required expertise.
- Knowledge Retrieval: Queries the internal knowledge base and past ticket solutions to find relevant resolutions.
- Draft Resolution Generator: Creates a proposed response or solution draft based on the classification and retrieved knowledge.
- Human Review Queue: Presents the ticket, suggested classification, and draft resolution to a human agent for approval, modification, or rejection.
- Action Executor: Upon approval, executes the resolution (e.g., sends response, updates ticket, triggers workflow).
- Audit Logger: Records every decision, action, and human intervention for compliance and improvement.
What Was Built
The system was implemented as a series of microservices communicating via a message queue, integrating with the client's existing ticketing system (treated as a black box with read/write API access). The LLM component used a managed API service with strict data handling agreements. All custom glue code and orchestration was developed in-house.
Measurable Outcome
The agentic triage layer handled a significant portion of incoming tickets with varying degrees of automation. First-line staff reported a measurable reduction in time spent on repetitive classification and routing tasks, allowing them to focus on more complex issues requiring human expertise. Time-to-first-response showed improvement as tickets were initially assessed and routed more rapidly.
Importantly, every customer-facing action remained under human oversight—agents could approve, modify, or reject the AI's suggestions, ensuring accountability and quality control.
Lessons Learned
Starting with a narrow, well-defined use case (ticket classification and routing) was crucial for managing complexity and demonstrating value quickly. Investing in high-quality prompt engineering and retrieval augmentation yielded better results than simply using a more powerful LLM. Maintaining human-in-the-loop for all customer-facing actions was non-negotiable for trust and safety.
One challenge was handling edge cases where the knowledge base lacked sufficient information—this highlighted the importance of continuous knowledge base updates and clear escalation paths for the AI when confidence fell below a threshold.
Why This Approach Worked
This case study demonstrates that agentic AI doesn't have to mean full automation or loss of control. By thoughtfully designing a system where AI handles the initial cognitive lift—classification, information retrieval, and drafting—while keeping humans firmly in the loop for judgment and action, we achieved efficiency gains without compromising service quality or security. The key was treating the AI as a powerful assistant to skilled professionals, not a replacement for them.