Structured Judgment
Evaluation where an AI judge scores work against explicit plans, tasks, evidence, and policies rather than subjective impressions.
Structured Judgment replaces "does this look good?" with "does this meet the criteria?" It works by defining evaluation criteria upfront — tied to the plan, the requirements, and the policies — and then scoring the agent's output against those criteria systematically. Each criterion gets a score, evidence is cited, and the overall assessment is grounded in specifics rather than vibes. This approach is reproducible, auditable, and resistant to the model's tendency to produce output that sounds impressive but doesn't actually meet requirements.
More in Evaluation
Policy-Bound Evaluation
Evaluation scored against explicit plans, tasks, evidence, and policies.
Eval Drift
Gradual misalignment between what an evaluation measures and what actually matters.
Judgment Bias
Systematic skew in AI evaluation due to unexamined assumptions, prompt framing, or training artifacts.
Rubric Rot
Decay in evaluation criteria relevance over time — the eval no longer tests what it should.
Eval Capture
When an agent optimizes for passing evaluation rather than doing the actual work.
Eval Integrity
Evaluation that remains aligned with real-world outcomes over time — resistant to Eval Drift, Rubric Rot, and Eval Capture.