The Bayesian Agent
Agentic reasoning is often based on Bayesian logic. The agent starts with a set of **Beliefs** (priors) about the world based on its training and past experience. When it receives new data (an observation), it performs a "Belief Update," adjusting its internal model to reflect the new reality. This allows the agent to maintain a "Stable yet Flexible" understanding of its environment.
Managing Bias in Priors
The "Priors" of an agent are its default assumptions. If an agent's priors are biased or outdated, its reasoning will be flawed. We manage this through "Belief Auditing," where we regularly check the agent's default assumptions against verified ground truth data. This ensures the agent's logic remains grounded in reality.
Conclusion
Beliefs are the foundation of action. By building agents that can rigorously manage and update their own internal models, we are creating a new level of intellectual integrity in artificial intelligence.