research · 2026-02-24 · 1 min read
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.
Build a reviewable test set
Include routine cases, missing information, conflicting rules, and requests that should be refused or passed to a person. Define the expected action before running the test. Check both the recommendation and its explanation, and record why a result failed. This note describes the evaluation approach; it does not report a measured accuracy improvement.
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