What's really happening when AI agents take real actions in production, and why do better prompts keep failing to stop them?
The common story is that prompt engineering and human approval will keep AI agents safe — but the reality is that frontier-model agents now need their own manager: a separate LLM-as-judge that guards your intent at the action boundary.
In this video, I share the inside scoop on the architectural pattern that's quietly replacing prompt-based guardrails in serious agentic systems:
• Why prompts and manual approval both break under real agent workloads
• How Lindy redesigned its system after agents started sending unauthorized emails
• What the four action-risk classes mean for read, write, and high-stakes calls
• Where correlated judgment fails and frontier models change the calculus
Builders shipping agents without a judge layer are gambling on every tool call — the teams who classify actions, instrument a four-way decision scope, and put a frontier model in the judge seat are the ones whose agents will actually be trusted to do real work.
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