On a recent Monday morning, a user engages two AI tools: one summarizes new emails, while the other analyzes a competitor’s growth. This second AI tool reviews financial reports, social media sentiment, and company sales data, ultimately drafting a strategy and scheduling a meeting to present its findings. While both tools are described as “AI agents,” they differ in intelligence, capabilities, and the trust users place in them. This disparity raises questions about development, evaluation, and governance in the AI landscape.
To understand AI agents, it is crucial to define them. According to the foundational AI textbook by Stuart Russell and Peter Norvig, an agent perceives its environment through sensors and acts using actuators. A thermostat, for instance, senses temperature and adjusts heating accordingly. Modern AI agents consist of four key components: perception, reasoning, action, and an overarching goal. While a basic chatbot responds to questions, it lacks an overarching goal and the ability to use tools independently.
Historical frameworks in other industries can guide the classification of autonomy in AI agents. The automotive industry’s SAE J3016 standard outlines six levels of driving automation, from manual driving to full autonomy, focusing on who handles real-time driving tasks and under which conditions. Aviation offers a more nuanced ten-level model, emphasizing collaborative human-machine interactions. The robotics field considers additional factors like human independence, mission complexity, and environmental stability.
Emerging frameworks for AI agents tend to fall into three categories: capability-focused frameworks detailing what agents can do; interaction-focused frameworks assessing collaboration between the user and the agent; and governance-focused frameworks addressing legal responsibilities and accountability. No single model fully encapsulates the complexities of AI agents, revealing gaps in understanding safe operational boundaries and the nuances of agent functionality.
Challenges persist, particularly concerning alignment, ensuring that an agent’s actions align with human intentions. As AI technology advances, it is vital to establish collaboration between humans and agents, paving the way for a future where they operate not independently, but as a network of specialized agents working alongside users. This collaborative model may provide a more effective and responsible approach as AI becomes increasingly integrated into daily life.
Source: https://venturebeat.com/ai/we-keep-talking-about-ai-agents-but-do-we-ever-know-what-they-are