Developing autonomous contact center agents requires robust pre-production evaluation harnesses to ensure policy compliance and prevent costly errors. Traditional unit testing is insufficient for these non-deterministic systems, necessitating an evaluation pipeline that generates synthetic edge cases, collects agent traces, and scores them against business rules. This approach involves components like a synthetic test case generator, a mock tool environment, a trace collector, and an LLM-as-a-Judge scorer to validate tool selection, parameter usage, and adherence to conversation policies before deployment. AI
IMPACT Ensures AI agents in customer-facing roles adhere to business rules and avoid costly errors.
RANK_REASON The item describes a method for testing AI agents, not a new AI model or research.
- application programming interface
- Ci Cd
- Contact Center Agents
- get_order_details
- PROCESS_REFUND
- submit_supervisor_ticket
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