From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
Introduces a "harness-engineering" approach that shifts deterministic logic from prompts to code, enabling auditable and traceable enterprise LLM agents. Validates the architecture using a dataset of 25 listed Korean companies, demonstrating robust performance across source grounding, entity routing, and output hygiene. Proves that code-enforced contracts maintain full utility (120/120) compared to external guardrails (88/120) which suffer from excessive refusal rates. Demonstrates that prompt-o
Analysis
TL;DR
- Introduces a "harness-engineering" approach that shifts deterministic logic from prompts to code, enabling auditable and traceable enterprise LLM agents.
- Validates the architecture using a dataset of 25 listed Korean companies, demonstrating robust performance across source grounding, entity routing, and output hygiene.
- Proves that code-enforced contracts maintain full utility (120/120) compared to external guardrails (88/120) which suffer from excessive refusal rates.
- Demonstrates that prompt-only instructions fail to prevent internal trace leakage, whereas the proposed harness completely blocks such violations.
Why It Matters
This research addresses the critical gap between experimental LLM prototypes and production-ready enterprise applications by providing a structured method for ensuring reliability and auditability. It offers practical insights for AI engineers on how to balance safety constraints with operational utility, showing that rigid code-based enforcement outperforms both pure prompting and external guardrails.
Technical Details
- Architecture: Reconstructs LLM agent patterns by moving deterministic behavior into code, manifests, schemas, and validation artifacts surrounding a replaceable composition boundary.
- Dataset: Evaluated on a public-data slice comprising five Korean corporate groups and 25 listed companies.
- Validation Scenarios: Tested for source-grounding, entity-routing, traceability, output-hygiene, and recommendation-language contracts.
- Model Substitution: Verified stability across three different hosted models, with all 270 composition-boundary runs passing checks.
- Ablation Study: Compared the harness against prompt-only instructions and bolt-on external guardrails, highlighting the trade-off between safety and utility.
Industry Insight
- Enterprises should prioritize code-based validation layers over prompt engineering alone to ensure consistent compliance and prevent data leakage in production environments.
- When implementing guardrails, teams must carefully tune refusal thresholds to avoid degrading user experience, as overly strict external filters can significantly reduce utility.
- Adopting a modular harness design allows for easier model substitution and maintenance, facilitating the transition from prototype to scalable, auditable AI products.
Disclaimer: The above content is generated by AI and is for reference only.