Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness
arXiv:2606.11686v1 Announce Type: new Abstract: End-to-end task-success is the dominant way to evaluate LLM agents, but one aggregate number tells you that an agent regressed, not where. We present layer-isolated evaluation: a deployed ordering agent is decomposed into a fixed taxonomy of layers (ontology, intent, routing, decomposition, escalation, safety, memory, and cross-cutting envelope/defense), each exercised by its own assertion slice in a deterministic, no-LLM "pure" mode. The pure suite (238 cases across 23 slices; 225 run in 2.39 s, ~10 ms/case) runs in CI on every change against a
The rapid development and deployment of LLM agents in production environments necessitate more robust and granular evaluation methods beyond simple end-to-end task success.
This development addresses a critical challenge in scaling LLM agent reliability and enables faster iteration and debugging, making agents more trustworthy for complex tasks.
The ability to pinpoint failures within specific 'layers' of an LLM agent improves development efficiency and operational stability, moving LLM agents closer to enterprise-grade reliability.
- · AI developers
- · Enterprises deploying LLM agents
- · AI-powered SaaS companies
- · Companies relying on brittle, un-debuggable LLM agent deployments
Improved reliability and faster development cycles for complex LLM agents.
Accelerated adoption of LLM agents across more sensitive and critical enterprise functions due to increased trust and lower operational risk.
The development of standardized testing and evaluation frameworks for AI agents, driving industrialization of the AI agent ecosystem.
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Read at arXiv cs.CL