Description
When an AI agent keeps making mistakes, the instinct is to add a safety net — put a human in the loop, have a second AI check the first, or wrap the model in guardrails. Dan Klein explains why each of these popular fixes tends to fall short, and why real reliability has to be built into the model rather than bolted on afterward.
Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group. He previously co-founded Semantic Machines (acquired by Microsoft in 2018) and built APT, a model designed from the ground up for reliable agentic AI.
🎧 Watch the full episode of DataFramed: https://www.youtube.com/watch?v=nPRn1o0VK58
Connect with Dan Klein: https://www.linkedin.com/in/dan-klein/
Scaled Cognition: https://www.scaledcognition.com/
Berkeley NLP Group: https://nlp.cs.berkeley.edu/
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