Evolving With AI

When I instruct an agent to perform different tasks, I often expect it to understand more than what I explicitly write in the instructions. In many situations, I know what I want to achieve, but I do not know how to describe the problem completely, especially when I am dealing with a topic that is unfamiliar to me. This makes proactive collaboration one of the most interesting possibilities I see in agentic AI. If an agent can recognize the meaning behind incomplete instructions, identify important implicit cues, and help me turn an unclear intention into a more concrete task, the interaction could become much more efficient. ...

When I work with an AI agent on a complex task, I find that the precision of my instructions can have a significant effect on how smoothly the work proceeds. A vague instruction may lead to results that require several rounds of clarification, correction, and confirmation. A more precise instruction can give the agent a clearer understanding of what I expect from the beginning, which can save time for both the human and the AI. ...

Where should we draw the capability boundary between AI and humans? It may be tempting to assume that AI can never be as productive or innovative as a human, but this assumption is becoming increasingly difficult to maintain. Consider video generation models as an example. A carefully designed prompt or set of instructions is itself a product of human thinking, but the resulting video can sometimes be remarkably detailed, coherent, and visually innovative. ...

How much should we expect to remain unchanged in our work and learning as agentic AI develops? I think agentic AI is something we cannot simply avoid; we will have to accept it and learn how to cope with it. Its capabilities are not fixed, but continuously accumulated and improved over time. For people who are not directly involved in AI development, it is difficult to predict what these systems will be able to do in the near future. This makes the idea that “AI should never replace us” difficult to maintain as a general principle. ...