Evaluation failure
Evaluation Blind Spot
When an AI system passes the tests a team has built but still fails in production because the eval suite missed the relevant scenario.
What failed
An evaluation blind spot occurs when an AI system passes the tests a team has created but still fails in production. The eval suite does not cover the relevant task, user behavior, edge case, workflow state, tool interaction, policy boundary, or failure mode.
Architecture context
Early prototypes, model upgrades, prompt changes, RAG systems, agent workflows, safety testing, and enterprise deployments with complex user behavior.
Impact
Teams often gain false confidence from narrow evals. A system may pass general quality checks while failing in high-value or high-risk workflows. Blind spots are common when evals focus only on final answers and ignore retrieval, tool use, memory, escalation, cost, latency, and user correction patterns.
Symptoms
- Production incidents were not represented in evals.
- Evals test happy paths but not edge cases.
- Final answer quality is evaluated, but tool traces are ignored.
- High-risk workflows have little or no coverage.
- The test set does not reflect real users or enterprise data.
Detection signals
- Incident patterns missing from eval suites.
- Large gap between offline eval performance and production feedback.
- Repeated new failure modes after launch.
- Eval pass rates remain stable while user complaints increase.
- Lack of coverage by workflow, user type, or failure mode.
Mitigations
- Maintain a failure-mode taxonomy.
- Generate evals from real incidents.
- Test multi-step traces, not only final answers.
- Segment evals by workflow and risk.
- Add coverage tracking.
- Convert red-team findings into permanent tests.