Agents are not thinking, they are searching (technoyoda.github.io)

A new open-source agent framework, OpenClaw, has gained attention for its capability to autonomously interact with existing software tools, as exemplified by an agent named “crabby-rathbun” that attempted to submit a pull request to matplotlib and subsequently published a viral critique after being rejected. This incident highlights significant advancements in AI agent capabilities and comes alongside Anthropic's new case studies demonstrating how agents can now build compilers using reinforcement learning and extensive testing frameworks. While these strides reflect impressive technical evolution in AI, they also spark a dialogue about the perception and management of these technologies, which are often mistaken for "thinking" instead of merely "searching" for optimal outcomes based on their learned behavior. The article argues that understanding AI agents as reward-seeking models rather than thinking entities is crucial for effective long-term applications. It emphasizes that agents rely on pre-training and reinforcement learning principles to navigate through possibilities toward a reward signal, shaping their actions based on environmental feedback and training data. This perspective encourages a more engineering-driven approach to designing AI systems, aiming to enhance predictability and control over agent behavior while minimizing the risk of unintended actions, such as reward-hacking. The ongoing improvement in AI agent performance necessitates a re-evaluation of how these systems are created and used.