Evaluation Governance

Evaluation governance establishes frameworks and policies for systematically assessing AI model performance, reliability, and compliance across development and deployment stages. This practice ensures organizations can verify that AI systems meet accuracy, fairness, safety, and regulatory requirements before and after production use. Strong evaluation governance is critical for mitigating risks, maintaining stakeholder trust, and documenting the decision-making processes that support responsible AI deployment at scale.

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ResearchGlobal2026-08-23

Red-Team Results Don't Transfer Across Agent Harnesses, NHIMG Finds

Research published by the NHI Management Group finds that autonomous agent evaluation outcomes depend materially on the harness, middleware, and gateway surrounding the model, not just the model itself. The analysis recommends standardizing approved harnesses, restricting tool exposure to task-scoped permissions, and treating the model plus its full harness stack as a single governed deployment unit. Organizations that have red-teamed models in isolation may hold test results that do not reflect production risk.

red teaming agentic AI agent harness tool scoping pre-deployment testing NHI Management Group evaluation governance