The idea of agentic systems taking over more complex human tasks is compelling.
New "production-grade" frameworks to build agentic systems pop up, suggesting that we're close to achieving full automation of these challenging multi-step tasks.
But is the underlying agentic technology itself ready for production?
And if not, can LLM-based systems help us making better decisions?
Recent new developments in the DoWhy/PyWhy ecosystem might bring some answers.
Will they—combined with new methods for validating causal models now available in DoWhy—impact the way we build and interact with causal models in industry?
*About The Guest* Amit Sharma is a Principal Researcher at Microsoft Research and one of the original creators of the open-source Python library DoWhy, considered the "scikit-learn of causal inference." He holds a PhD in Computer Science from Cornell University. His research focuses on causality and its intersection with LLM-based and agentic systems. Amit deeply cares about the social impact of machine learning systems and sees causality as one of the main drivers of more useful and robust systems.
*About The Host* Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4 ).
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