Harness Engineering Creates Auditable Enterprise LLM Agents

Enterprise applications built with Large Language Models (LLMs) often start as prompt-driven prototypes, which lack the rigor needed for production. Researchers propose a "harness-engineering" approach to evolve these into traceable and auditable LLM agent architectures. This method shifts deterministic behaviors, such as source boundaries, entity routing, and answer formats, from prompts into explicit code, manifests, schemas, and validation artifacts. This creates a robust "composition boundary" around the LLM, ensuring predictable and verifiable outputs. The evaluation demonstrated that this harness preserves critical contracts like source-grounding and output hygiene across various scenarios and model substitutions. Crucially, it showed that code-owned guarantees are load-bearing and cannot be replicated by prompting alone; prompt instructions failed to prevent violations like recommendation-language issues or internal trace leakage, which the harness entirely blocked. While external guardrails could also prevent violations, they often reduced utility, whereas the harness maintained full utility alongside safety. This pattern offers a reusable engineering solution for productizing LLM prototypes into reliable, auditable applications.