Probabilistic Nondeterminism
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Boosting AI Agent Scalability by Decoupling Logic and Search
Separating core agent logic from execution strategies is crucial for scalability. Researchers propose Probabilistic Angelic Nondeterminism (PAN) and the ENCOMPASS framework, which allows developers to define the “happy path” of an agent’s workflow while deferring inference-time strategies to a runtime engine. This decoupling reduces technical debt and enhances performance, enabling independent optimization of logic and search algorithms without code modification.
