Green Lights, Wrong Answers: Trusting AI Agents That Fail Silently

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Green Lights, Wrong Answers: Trusting AI Agents That Fail Silently

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Before a recent launch, an AWS team's internal AI agent turned $410.08 of a customer's revenue into '$410.08 Million' — fluent, confident, and wrong, while every monitoring dashboard stayed green. Daniel and Emily dig into why agents fail silently, whether evaluation can become the operating system of trust, and what's left for human judgment when the machine drafts the answer. ## Hosts ### Daniel Altman Daniel Altman is a computer scientist and AI strategist with a background in engineering, quantitative research, and emerging technologies. He studies how technological breakthroughs reshape industries, markets, work, and human capabilities. ### Emily Carter Emily Carter is a business strategist and former technology product leader with experience building digital products and studying how innovation reaches millions of users. She focuses on AI adoption, business transformation, work, education, entrepreneurship, and leadership. ## Sources - https://aws.amazon.com/blogs/machine-learning/building-trust-in-ai-agents-a-production-ready-evaluation-framework-with-amazon-bedrock-agentcore/ ## Episode Information Language: en Published: 2026-10-17T21:26:30.455064+00:00

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