Deterministic and agentic AI hybrid model
For high-risk, data-intensive domains like corporate finance, AI adoption requires a hybrid architecture combining deterministic machine learning with agentic workflow orchestration.
The argument
The speakers argued that generative AI alone is insufficient for finance because mathematical accuracy is non-negotiable. A successful deployment requires a three-part blueprint: a reliable and comprehensive data layer, deterministic models for calculations, and agentic frameworks to handle workflow reasoning and exception handling.
The thesis, stress-tested
✓ What validates it
- ✓Increased adoption of agentic workflows in other highly regulated back-office domains like legal compliance and KYC
▸ Risks discussed
- ▸Lack of trust in fully autonomous agentic AI systems
- ▸System failure if generative models are mistakenly used for deterministic mathematical tasks
Hear it yourself
"And in fact, David, like, that is the reason why I love so many things about the Stack story and what you've just been describing, Albert, because almost two, three years ago, when we went from the kind of traditional AI world into the generative AI world, there were certainly some pockets or some business lines that were fast movers,…"
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