Theory: Evaluation + Quality Assurance

Overview

Evaluation is the quality backbone of agent systems. Without repeatable evals, improvements are guesswork and regressions are discovered too late. A good eval strategy combines deterministic checks, rubric-based judgments, and trend monitoring over time.

Regression gates convert quality policy into release decisions. They ensure that code changes, prompt updates, and model swaps cannot silently degrade behavior.

Learning Ladder

Level	Focus	Outcome
Beginner	Eval case and rubric basics	Reproducible scoring
Intermediate	Baseline comparison	Detect regressions quickly
Advanced	Gate policy in delivery pipelines	Block unsafe releases

Core Concepts

Beginner Foundation

• Define expected behavior clearly for each eval case.
• Use deterministic checks where possible and rubrics where needed.
• Track correctness, latency, and safety as separate metrics.
• Keep eval datasets versioned and reproducible.

Intermediate Mechanics

• Maintain baselines per prompt, model, and major workflow version.
• Compare every candidate run to baseline deltas.
• Classify failures by category to target fixes efficiently.
• Add minimum sample sizes for reliable comparisons.

Advanced Production Patterns

• Enforce release gates with hard thresholds on critical metrics.
• Maintain adversarial eval subsets for abuse and safety coverage.
• Monitor judge-model drift and rubric consistency over time.
• Add shadow evaluation for major architecture or model changes.

Key Takeaways

• Evaluation must be continuous, versioned, and tied to release policy.
• Baselines are only useful when tracked per meaningful system version.
• Strong gates protect users from silent quality regressions.