Aaron Levie：面向特定任务的自主智能体正成为 AI 新形态
Pretty cool that we clearly have an emerging form factor for what AI might look like for the foreseeable future as autonomous systems dedicated to specific task
Box CEO Aaron Levie 认为，面向特定任务或领域的自主智能体正在成为 AI 可预见未来的新兴形态，远超 LLM 套壳阶段。他给出三点支撑：模型的工具与计算机使用能力提升，让智能体获得更细粒度的外部系统访问权，甚至自行构建缺失的软件；模型在法律、生物研究、代码安全、供应链等垂直领域的判断力增强；沙箱、文件系统、数据管道等基础设施支持更长时间运行和超大规模数据处理。这类系统已在 IT 自动化、网络安全运营、药物发现、制造业和 AI 研究中出现。
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Pretty cool that we clearly have an emerging form factor for what AI might look like for the foreseeable future as autonomous systems dedicated to specific tasks or domains.
This is a huge leap past the LLM wrapper days because the new expectation is that you can finally farm out more sophisticated work to the agent to tackle for you. And you can reliably trust that work can get accomplished properly (still with a human in the loop) because of far better tool and computer use, and the general intelligence of the models improving.
1) The tool and computer use capability increases in the models mean that you can provide the agent with far more granular access to external data systems and other software to operate. This means, in effect, the agent begins to execute workflows similar to how a person would have in that same function. And where there isn’t existing software, the agent just builds it itself. This is a huge unlock.
2) Next, the actual capability of the models are improving in just about every vertical domain. The models can make far better judgment calls in legal, do deeper bio research, scan for security issues in code, understand supply chain processes, and more. Obviously in many of these spaces then models still have worse intuition than people, but that will only get better.
3) Finally, the harnesses and surrounding infra — sandboxes, file systems, data pipelines, etc. — allows for longer running agents, agents to be able to work on arbitrarily complex or long tasks, process data at enormous scale, and more. Few of these components worked well for AI even a year ago, but we now have many of the core primitives necessary for more autonomous systems.
When combined with the right UX, implementation, and change management for the human oversight and coordination, it’s clear that these autonomous systems will become quite powerful in a wide variety of enterprise workflows.
We’re starting to see them in IT automation, cyber operations, drug discovery, manufacturing, AI research, and more spaces. You can almost imagine one or more of these systems for every role in the economy. Quite a bit of opportunity ahead.