Composite example: Customer triage assistant for a small service team

This composite case illustrates how a small customer service team used the design patterns above to build an effective agent without heavy engineering.

Problem

The team spent large portions of the day triaging inbound service requests. Managers wanted to reduce time-to-triage and speed routing to specialized teams.

Approach

1. Outcome: Reduce triage time per ticket by 40% while maintaining routing accuracy above 90%.
2. Scope: An assistive agent that reads the incoming ticket and suggests a classification and next actions; a human reviewer confirms and sends the response.
3. Intake and readiness: The team used the intake form to specify data sources (ticket system, knowledge articles), stakeholders, and acceptance tests.
4. Prototype: Prompt-based prototype using anonymized tickets and manual copy-paste integration. Ten acceptance scenarios were created (including edge cases like ambiguous requests and escalations).
5. Pilot: Two agents piloted with three triage specialists for four weeks; feedback buttons and a short review queue captured failures.

Outcomes and lessons

• The pilot met the target: triage time dropped by ~45% on average; routing accuracy stayed above 92% per acceptance tests.
• Key improvements came from enriching the agent’s knowledge with a short set of canonical KB articles and adjusting prompts for ambiguous language.
• Operational readiness (logging, rollback switch, and an owner) prevented a small incident from affecting customers when a KB feed was temporarily unavailable.

Why it worked

The team focused on a single, user-centered outcome, used short acceptance tests to measure results, and kept a human in the loop for uncertain cases. They delayed heavy integrations until the agent proved its value.