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All digests tagged Risk Management
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How do you give AI agents real autonomy without catastrophic failure? John Feminella details five composable controls—from Agent Registry to Classifier use—to govern non-deterministic AI systems.
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AI failures require a holistic, risk-based evaluation framework. Assess systems across Technical Strength, Deployment Controls, and Business Context using a 'build-break-fix' loop to ensure safety.
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AI agents must earn trust by proving measurable value, establishing clear accountability via the Three Lines Model, and maintaining auditable evidence through artifacts like the Model Inventory Record.
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Druid AI presents a framework to evaluate AI agents, arguing that simple metrics like containment rate are misleading. They propose a multi-pillar scorecard focusing on governance, risk, and business outcomes.
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Voice agents are scaling fast, but reliability is the number one problem. The speaker outlines a continuous monitoring loop, emphasizing that centralized AI systems require deep adversarial testing to prevent massive failures.
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