Show HN: An "earned autonomy" architecture for AI agents using Subjective Logic (kenschachter.substack.com)

A new framework has been proposed for AI agents, called an "earned autonomy" architecture, utilizing Subjective Logic to enhance decision-making capabilities. This architecture allows AI systems to dynamically adjust their autonomy based on the trust and performance metrics evaluated during interactions in various environments. By enabling agents to earn trust, the framework fosters a more nuanced approach to autonomy, where decision-making can evolve based on past experiences and outcomes. This development is significant for AI and machine learning as it addresses the longstanding challenge of balancing autonomy and control in AI systems. In practical applications, this means that agents can become more reliable over time, responding with greater accuracy in scenarios that involve human interaction or complex, dynamic conditions. Key technical aspects include the use of subjective probability to model uncertainty in agent decisions, paving the way for AI systems that can better navigate variability in real-world environments. Overall, this architecture represents a promising step toward building AI agents that not only perform tasks but do so with a growing understanding of their environments and interactions.