Having access to multiple models in one platform is powerful. Being able to systematically evaluate them on your data, with your quality criteria, is what makes that power actionable.
Generic benchmarks won't tell you which model handles your specific use case best. You need to evaluate on realistic scenarios, define what "good" means for your application, and compare models at scale.
We built a full evaluation workflow using MLflow on Databricks to show how this works in practice. The key steps:
- Build diverse test cases: not just happy paths, but edge cases, ambiguous situations, and multi-factor decisions
- Define complementary judges: trace-aware judges that verify decisions are grounded in actual tool outputs, plus template judges that assess reasoning quality
- Add human feedback: review judge assessments and provide corrections to align them with your standards
- Align your judges with human feedback: automatically improve judge instructions based on the patterns in your annotations
- Compare models systematically: run multiple models through the same evaluation pipeline with aligned judges
The technical post walks through this using Casper's Kitchens, a realistic ghost kitchen environment running on Databricks. It's a complaint triage agent that queries Unity Catalog functions, analyzes customer issues, and recommends credits or escalations.
This evaluation pattern works for any tool-calling agent: code assistants, retrieval systems, domain-specific workflows. If your agent makes decisions based on data it retrieves, you need judges that can verify those decisions are grounded in what the tools actually returned.
📖 Read the full walkthrough: https://lnkd.in/gkKbK5QR
🧪 Try the make_judge functionality in MLflow to build your own trace-aware and template-based judges
🛠️ Explore Casper's Kitchens: https://lnkd.in/gbFpSkAW
📅 Join us 11/11 for "The Future of AI: Build Agents that Work" with Sam Altman and Ali Ghodsi: https://lnkd.in/gp2Mnutv
#AI #Databricks #agents #MLflow
