Why Finance AI Agents Need Evaluation Harnesses, Not Just Better Prompts

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AI-powered finance agents can generate confident-sounding but incorrect decisions, making prompt engineering alone an insufficient quality control strategy for accounting automation. Experts recommend building an evaluation harness — a repeatable test system that runs realistic financial scenarios through an agent and scores outputs against predefined expectations. A robust test dataset should include routine, missing-evidence, conflicting, and adversarial cases, with expected behaviors stored as structured data to enable regression testing. Rather than relying on a single quality score, evaluations should be layered across schema validity, evidence grounding, policy compliance, and decision accuracy. This approach ensures that agent behavior remains auditable, within authority boundaries, and reliable enough for production use.

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