Trusting what you built:

Multi-agent AI systems are moving fast into financial services, life sciences, and the public sector, but the evaluation and reproducibility methods enterprises inherited from classical machine learning don't hold up under regulatory scrutiny.

This white paper shows data science and MLOps leaders how to reframe both: trade a single accuracy score for a defensible profile spanning task success, decision quality, tool-use correctness, and human intervention, and trade an exact rerun for a documented, bounded range of behavior built on versioned data, captured environments, recorded model versions, and stored prompts.

With Gartner projecting that more than 70 percent of AI agent projects will fail by 2029, and just 26 percent of enterprises reporting fully integrated AI governance, the framework gives practitioners in FINRA-regulated trading and surveillance, 21 CFR Part 11 clinical workflows, and public-sector benefits determinations a practical, ten-point checklist for making agentic AI auditable, explainable, and ready to stand behind in front of a regulator, model risk reviewer, or auditor.