[simonw]

This paper does at least lead with its version of what "agents" means (I get very frustrated when people talk about agents without clarifying which of the many potential definitions they are using):

> An agent, in the context of AI, is an autonomous entity or program that takes preferences, instructions, or other forms of inputs from a user to accomplish specific tasks on their behalf. Agents can range from simple systems, such as thermostats that adjust ambient temperature based on sensor readings, to complex systems, such as autonomous vehicles navigating through traffic.

This appears to be the broadest possible definition, encompassing thermostats all the way through to Waymos.

[adpirz]

You posted on X a while back asking for a crowdsourced definition of what an "agent" was and I regularly cite that thread as an example of the fact that this word is so blurry right now.

[simonw]

I really need to write that up in one place - closest I've got is this section from my 2024 review https://simonwillison.net/2024/Dec/31/llms-in-2024/#-agents-...

[DebtDeflation]

>"The two main categories I see are people who think AI agents are obviously things that go and act on your behalf—the travel agent model—and people who think in terms of LLMs that have been given access to tools which they can run in a loop as part of solving a problem."

This is exactly the problem and these two categories nicely sum up the source of the confusion.

I consider myself in the former camp. The AI needs to determine my intent (book a flight) which is a classification problem, extract out the relevant information (travel date, return date, origin city, destination city, preferred airline) which is a Named Entity Recognition problem, and then call the appropriate API and pass this information as the parameters (tool usage). I'm asking the agent to perform an action on my behalf, and then it's taking my natural language and going from there. The overall workflow is deterministic, but there are elements within it that require some probabilistic reasoning.

Unfortunately, the second camp seems to be winning the day. Creating unrealistic expectations of what can be accomplished by current day LLMs running in a loop while simultaneously providing toy examples of it.

[swyx]

didnt your summary https://gist.github.com/simonw/beaa5f90133b30724c5cc1c4008d0... pretty much cover it?

[adpirz]

Whoa missed this! Love it.

[ethbr1]

The language problem around agents is that most companies are describing them solely from a human/UX perspective.

'You ask it to do something, and it does it'

That makes it difficult to differentiate the more critical 'how' options in the execution process. From that perspective: deterministic integrations, LLM+tools, LAM, etc are more descriptive categories, each with their own capabilities, strengths, and weaknesses.

Or to put it a different way, if the term doesn't tell you what something is good and bad at, it's probably an underspecified term.

[sanjin]

I took a crack at it here that tries to bridge the gap from "autonomous" which is just software to that Agentic autonomy - https://www.aiimpactfrontier.com/p/framework-for-ai-agents

[adpirz]

This write up was also fantastic and has made the rounds at our org!

[jvans]

this is a great write up, thank you

[mindcrime]

It's been blurry for a long time, FWIW. I have books on "Agents" dating back to the late 90's or early 2000's in which the "Intro" chapter usually has a section that tries to define what an "agent" is, and laments that there is no universally accepted definition.

To illustrate: here's a paper from 1996 that tries to lay out a taxonomy of the different kinds of agents and provide some definitions:

https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&d...

And another from the same time-frame, which makes a similar effort:

https://www.researchgate.net/profile/Stan-Franklin/publicati...

[ethbr1]

The technical difference between agents then and agents now are the fuzzy parameter mapping capabilities of LLMs, if used.

Scaling agent capability requires agents that are able to auto-map various tools.

If every different tool is a new, custom integration, that must be written by a person, then we end up where we are today -- specialized agents where there exists enough demand and stability to write and maintain those integrations, but no general purpose agents.

Ultimately, parameter mapping in a sane, consistent, globally-applicable way is the key that unlocks an agentic future, or a failure that leads to its demise.

[williamcotton]

So basically just the concept of feedback in a cybernetic system.

https://en.wikipedia.org/wiki/Cybernetics

[bob1029]

> The field is named after an example of circular causal feedback—that of steering a ship (the ancient Greek κυβερνήτης (kybernḗtēs)...

Now that name makes a lot more sense to me.

[openrisk]

Which is also the root of the word 'government', so a government agent is doubly cybernetic in a sense

[8338550bff96]

Then is "agents" just non-spooky coded language for "cyborgs"

[throw5959]

I studied cybernetics. Our teachers called us "cybernets".

[cratermoon]

We're at the phase of the hype cycle where "agent" means whatever the marketing materials want it to mean.

[behnamoh]

People have been talking about agents for at least 2 years. Remember when AgentGPT came out? How's that going so far? Agents are just LLMs with structured output, which often happens to be a JSON with info about a function arguments to be called.

[mindcrime]

> People have been talking about agents for at least 2 years.

WAY longer than that. What's come to the forefront specifically in the last year or two is very specific subset of the overall agent landscape. What I like to call "LLM Agents". But "Agents" at large date back to at least the 1980's if not before. For some of the history of all of this, see this page and some of the listed citations:

https://en.wikipedia.org/wiki/Software_agent

> Agents are just LLMs with structured output

That's only true for the "LLM Agent" version. There are Agents that have nothing to do with LLM's at all.

[simonw]

Right - the term "user-agent" shows up in the HTTP/1.0 spec from 1996: https://datatracker.ietf.org/doc/html/rfc1945 and there's plenty of history of debates about the meaning of the term before then.

In 1994 people were already complaining that the term that had no universal agreed definition: https://simonwillison.net/2024/Oct/12/michael-wooldridge/

[mindcrime]

Yes. I am fond of saying "If you're talking about agents and think the term is something new, go back and read everything Michael Wooldridge ever wrote before talking any further". :-)

[curious_cat_163]

Yes, and the definition works reasonably well for the core arguments they are making in Section 5.

I suspect they'll follow up with a full paper with more details (and artifacts) of their proposed approach.

[htrp]

agents are the 2020s version of data science in the 2010s

[Kerbonut]

Do you mean that agents are being hyped in the same way data science was in the 2010s, or that they’ll have a similar impact over time? Would love to hear more of your thoughts.

[snapcaster]

I think he meant it's a similarly blurry term

[simonw]

Yeah, what does "data science" mean, exactly?

[throw5959]

Using the scientific method to handle data.

[baxtr]

To me "Agents" sound like computer programs that interact through APIs?

[bsenftner]

Oh come on! You and I know very well an AI Agent is anything marketing says they are, and that is the absolute final truth.