Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness

Researchers present layer-isolated evaluation, a deterministic testing framework that decomposes LLM agents into eight functional layers, each validated independently without requiring LLM execution. Testing across 238 cases reveals that aggregate end-to-end metrics mask localized regressions, with targeted layer failures causing 25-91 percentage point drops in component-specific tests while barely affecting overall pass rates.

This research addresses a critical gap in LLM agent evaluation methodology. Production systems typically rely on aggregate end-to-end success metrics, which provide limited diagnostic value when failures occur. The layer-isolated approach decomposes a deployed ordering agent into ontology, intent, routing, decomposition, escalation, safety, memory, and cross-cutting layers, each tested independently in deterministic, LLM-free mode. The framework runs 238 test cases in 2.39 seconds, enabling rapid CI/CD integration without computational overhead.

The masking effect discovered here carries significant implications for AI system reliability. When researchers deliberately degraded individual layers, aggregate pass rates dropped only 1.7-5.9 percentage points across six non-safety layers, yet corresponding component-specific tests plummeted 25-91 percentage points. This dramatic disparity indicates that traditional metrics fail to expose localized component failures that broader orchestration logic compensates for automatically. The phenomenon replicates across different infrastructure (Starbucks SG tenant), confirming it transcends single-catalog artifacts.

For production AI systems, this framework solves a practical engineering problem. Developers gain precise regression localization without manual root-cause analysis, reducing debugging cycles and enabling confident deployment velocity. The deterministic nature eliminates flakiness inherent to stochastic testing, while the sub-second execution overhead integrates seamlessly into continuous deployment pipelines. The coverage-honesty criterion prevents engineers from claiming untested layers work correctly.

Future adoption depends on framework generalization beyond ordering agents. The methodology represents a concrete instantiation of component-level evaluation that emerging MLOps practices prescribe but rarely implement rigorously.

• →Layer-isolated evaluation identifies component-level failures that aggregate metrics systematically mask, with 25-91pp drops in specific layers while overall success rates change only 1.7-5.9pp
• →Deterministic, LLM-free testing of 238 cases completes in 2.39 seconds, enabling real-time CI/CD validation without computational overhead
• →Regression injection validation across seven layers confirms per-slice baselines correctly localize faults to their source in 5 of 7 cases and top-3 in all 7 cases
• →Framework decomposes agents into eight distinct layers: ontology, intent, routing, decomposition, escalation, safety, memory, and envelope/defense
• →Coverage-honesty criterion prevents false confidence in untested components, improving reliability assessment accuracy for production systems