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4 articles from Crelis on Guardrails. Most recent: “Deterministic Policy or LLM-as-Judge: Which One Can You Defend to an Examiner?”.
An LLM judge can return a different verdict on the same facts tomorrow; a deterministic policy engine returns the same decision each time and can name the rule that made it — that difference decides w
The gap between an AI's proposal and an enterprise's permission is where catastrophic liability lives. Trust is a systemic vulnerability. You recognize that LLM agents exhibit unpredictable emergent b
Ungoverned AI agents are not assets. They are liabilities. Without a verifiable governance layer, your production models operate in a vacuum of accountability.
An autonomous agent's mandate is only as strong as the infrastructure that constrains it. Accountability for unauthorized actions is a matter of architectural integrity, not better model training.