Welcoming Diverse Approaches Keeps Machine Learning Strong What technology counts as an “agent”? Instead of arguing, let's consider a spectrum along which various technologies are “agentic.”

Published
Jun 12, 2024
Reading time
2 min read
Welcoming Diverse Approaches Keeps Machine Learning Strong: What technology counts as an “agent”? Instead of arguing, let's consider a spectrum along which various technologies are “agentic.”

Dear friends,

One reason for machine learning’s success is that our field welcomes a wide range of work. I can’t think of even one example where someone developed what they called a machine learning algorithm and senior members of our community criticized it saying, “that’s not machine learning!” Indeed, linear regression using a least-squares cost function was used by mathematicians Legendre and Gauss in the early 1800s — long before the invention of computers — yet machine learning has embraced these algorithms, and we routinely call them “machine learning” in introductory courses!

In contrast, about 20 years ago, I saw statistics departments at a number of universities look at developments in machine learning and say, “that’s not really statistics.” This is one reason why machine learning grew much more in computer science than statistics departments. (Fortunately, since then, most statistics departments have become much more open to machine learning.)

This contrast came to mind a few months ago, as I thought about how to talk about agentic systems that use design patterns such as reflectiontool useplanning, and multi-agent collaboration to produce better results than zero-shot prompting. I had been involved in conversations about whether certain systems should count as “agents.” Rather than having to choose whether or not something is an agent in a binary way, I thought, it would be more useful to think of systems as being agent-like to different degrees. Unlike the noun “agent,” the adjective “agentic” allows us to contemplate such systems and include all of them in this growing movement.

More and more people are building systems that prompt a large language model multiple times using agent-like design patterns. But there’s a gray zone between what clearly is not an agent (prompting a model once) and what clearly is (say, an autonomous agent that, given high-level instructions, plans, uses tools, and carries out multiple, iterative steps of processing). 

Rather than arguing over which work to include or exclude as being a true agent, we can acknowledge that there are different degrees to which systems can be agentic. Then we can more easily include everyone who wants to work on agentic systems. We can also encourage newcomers to start by building simple agentic workflows and iteratively make their systems more sophisticated. 

In the past few weeks, I’ve noticed that, while technical people and non-technical people alike sometimes use the word “agent,” mainly only technical people use the word “agentic” (for now!). So when I see an article that talks about “agentic” workflows, I’m more likely to read it, since it’s less likely to be marketing fluff and more likely to have been written by someone who understands the technology.

Let’s keep working on agentic systems and keep welcoming anyone who wants to join our field!

Keep learning,

Andrew 

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