# Review Architecture > [!summary] > Eval Labs now separates fast employee judgment from senior adjudication so Lucia can learn from structured signal without letting non-expert reviewer language become training truth. --- ## The three-layer review model Eval Labs review is not one flat annotation step. It is a layered judgment system: ```text Layer 1: Employee Review → Layer 2: Escalation Routing → Layer 3: Senior Adjudication ``` Each layer has a different job. --- ## Behavioral Observatory position Behavioral Observatory adds a saved behavioral label layer beside the Review Queue workflow. It does not erase the three-layer review model. Use this distinction: ```text Review Queue = score and review the prompt/response item. Behavioral Observatory = save structured behavioral labels for the conversation. Registry Diagnostics = inspect derived classification suggestions. ``` Behavioral Observatory labels can become useful behavioral evidence, but they should not be confused with senior adjudication or Gold Standard approval. --- ## Layer 1 — Employee Review Employee Review captures fast human reaction. It is designed for reviewers who may not know AI, prompting, ontologies, or model training. Employees answer guided questions such as: ```text Did Lucia understand what was needed? Did Lucia give the right next move? Did Lucia make the situation feel calmer? Did anything feel risky, confusing, or wrong? Should a senior reviewer look at this? Could this teach Lucia something reusable? ``` This layer should be: <ul class="eval-status-list"> <li>simple</li> <li>fast</li> <li>structured</li> <li>low-friction</li> <li>non-technical</li> <li>psychologically clear</li> </ul> Employee reviewers should not be asked to invent labels, taxonomies, intent categories, action classes, or training language. --- ## Layer 2 — Escalation Routing Escalation Routing converts guided review answers into workflow state. Examples: ```text seniorReview = true → reviewState: needs_adjudication reusableLearning = true → canonCandidate: true riskOrConfusion = slightly_off → reviewState: needs_review riskOrConfusion = definitely_wrong → reviewState: needs_review ``` The important doctrine: ```text The employee reports what they experienced. The system routes the case. The senior reviewer interprets meaning. ``` --- ## Layer 3 — Senior Adjudication Adjudication is the senior-review layer where canonical meaning is assigned. Adjudication may include: - final human labels - final intent interpretation - follow-through decision - final action type - emotional read - owner pressure level - reason for the final call - reusable canon/training signal This layer exists to prevent ontology drift. Employees should not train Lucia directly with improvised language. --- ## Why this architecture matters Without this separation, Eval Labs risks collecting inconsistent reviewer opinions as if they were stable training truth. That creates: ```text ontology drift label noise inconsistent training signal reviewer fatigue low-confidence exports ``` With this separation, Eval Labs captures simple human judgment while preserving a high-quality senior interpretation layer. --- ## Canon rule > [!warning] > Non-expert reviewers provide signal. Senior adjudication provides meaning. This is the core protection layer for scalable human evaluation.