research - 2026-02-24

Research Log: Benchmarking Recommendation Quality in Agent Systems

We are building a compact evaluation set to compare recommendation quality across scenarios, confidence levels, and policy constraints.

Evaluation quality determines whether recommendation systems are trustworthy in production. Our current benchmark focuses on scenario diversity, policy edge cases, and confidence calibration under incomplete business context.

The objective is not only higher accuracy on static test data. We are optimizing for stable decision quality over time, with explainability signals that help operators understand and approve agent behavior.

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