OPAL Evals
Production-grade AI evaluation framework using LLM-as-a-Judge for scoring conversational agent outputs across 5 quality dimensions.
Overview
OPAL Evals is a production-grade AI evaluation framework that scores conversational agent outputs using multi-model LLM-as-a-Judge (Gemini, Claude) across 5 quality dimensions.
Architecture
- Stateless evaluation engine API for scalable, on-demand scoring
- CI/CD pipeline integration via GitHub Actions with threshold-based release gating
- Internal experimentation UI for prompt iteration and A/B testing of agent behaviors
Tech Stack
Python FastAPI Gemini Claude GitHub Actions GKE Datadog
Impact
- Reduced agent debugging time from 2-3 days to under 4 hours
- Enabled 5x faster prompt iteration cycles
- Tested across 50+ test scenarios
- On the process of adoption by enterprise customers for AI compliance requirements