arXiv:2607.08028v1 Announce Type: cross Abstract: Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context. Productization adds requirements for source boundaries, entity routing, answer contracts, and reproducible traces. We present a harness-engineering approach that reconstructs this pattern into a traceable, auditable LLM-agent architecture: deterministic behavior moves into code, manifests, schemas, and validation artifacts around a replaceable composition boundary, while source-backed claims remain the authori
The proliferation of LLM applications in enterprise settings necessitates more robust, auditable, and production-ready architectures to move beyond experimental prototypes.
This development addresses critical challenges in deploying AI agents responsibly and reliably within regulated and complex enterprise environments, enabling wider adoption.
The focus shifts from ad-hoc prompt engineering to structured 'harness engineering' for LLMs, treating agents as auditable software components rather than black boxes.
- · Enterprise software vendors
- · Compliance and audit firms
- · Organizations adopting LLM agents
- · AI agent developers
- · Companies relying solely on prompt engineering for production LLM systems
- · Unstructured LLM deployment frameworks
Enterprise LLM applications become more reliable, auditable, and scalable, accelerating their integration into core business functions.
Increased trust and regulatory acceptance of AI agents lead to broader market adoption and new use cases in sensitive sectors.
The standardization of LLM agent architectures could foster a common interoperable ecosystem, reducing vendor lock-in and stimulating innovation across the AI supply chain.
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Read at arXiv cs.CL