From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
Summary
A harness-engineering approach transforms prompt-dominant enterprise LLM prototypes into traceable, auditable architectures by shifting deterministic behavior into code, manifests, schemas, and validation artifacts, while source-backed claims remain authoritative. This method was instantiated on a public-data slice of five Korean corporate groups, covering 25 listed companies and 113 source-backed runtime claims. Evaluation confirmed the harness preserves source-grounding, entity-routing, trace, output-hygiene, and recommendation-language contracts across fixed validation scenarios and under model substitution across three hosted models. Crucially, code-owned guarantees proved load-bearing, preventing violations that prompt instructions alone or external guardrails (which over-refused, dropping utility to 88/120 compared to the harness's 120/120) could not. The result is a reusable engineering pattern for auditable applications with versioned source, control, and validation artifacts.
Key takeaway
For AI Engineers productizing LLM prototypes for enterprise use, relying solely on prompt instructions for critical behavior introduces significant auditability and reliability risks. You should adopt a harness-engineering approach to shift deterministic product rules into explicit code, schemas, and validation artifacts. This ensures source grounding, entity routing, and output hygiene are enforced, not merely instructed, providing auditable traces and preserving utility where external guardrails might over-refuse.
Key insights
Shifting LLM agent behavior from prompts to auditable, code-owned contracts ensures enterprise reliability.
Principles
- Deterministic LLM behavior belongs in code.
- Source-backed claims must be authoritative.
- Validation contracts ensure output integrity.
Method
The harness-engineering method reconstructs prompt-dominant LLM prototypes by moving product rules (source eligibility, claim selection, answer structure) into explicit code, manifests, schemas, and validators, governed by a control layer.
In practice
- Implement source manifests for knowledge base control.
- Promote claims to source-backed for provenance.
- Use validation gates for output hygiene.
Topics
- Harness Engineering
- LLM Agents
- Enterprise AI
- Auditability
- Retrieval-Augmented Generation
- Prompt Engineering
Code references
Best for: AI Architect, CTO, VP of Engineering/Data, AI Engineer, Machine Learning Engineer, MLOps Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.