Practical Guide: Building Closed-Loop Evals for Multimodal AI Agents
MaryamMiradi · x · 2026-08-12
The hardest problem in multimodal AI evals is that models can improve image pixels while altering the truth (e.g., menu says 8 wings, photo shows 6). Using Uber Eats' massive food catalog as an example, the author shares a 7-step architecture for building closed-loop evals against AI slop:
- Build Human Ground Truth: Sample real images across geographies and label them with a clear rubric to create a golden set.
- Route with a Multimodal Rubric: Evaluate image, text, and metadata together; score quality, content, and dish match.
- Check Cross-Modal Faithfulness: Compare image contents against text descriptions for counts and ingredients.
- Add a Multidimensional QA Gate: Compare original vs. generated images to reject unauthorized edits.
- Retry with QA Feedback: Feed failure reasons back into generation and measure Pass @ K.
- Learn from Production Drift: Sample fresh production traffic with human labels to catch new failure modes.
- Auto-Tune, Benchmark, Ship: Diagnose agent failures, update prompts, and re-test before rollout.
The author stresses logging everything—inputs, routing, prompts, and outcomes—as evaluation is impossible without full traces.
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