A Probabilistic / Bayesian Agent Model [D]

Explain Like I'm Normal

This conceptual framework argues for moving away from linear prompt-response cycles toward a Bayesian approach where agents maintain an internal state (Prior). By treating tool outputs as new evidence to update beliefs, builders can create systems that handle uncertainty and iterative discovery more effectively than static pipelines. It shifts the focus from 'better prompts' to 'better recursive logic.'