[dd8601fn]

Maybe someone knows, but it seems like the model used to be called the model, and the thing using a model (handling prompts and context and tool calling and feeding the model) used to be called the agent.

Are we now calling the model the agent and the agent the harness?

[arjie]

The nomenclature that makes sense for me is that the agent is the combination of the harness and the model. The model provides text-completion, the harness provides the loop around it, and the agent is the full structure of both.

However, nomenclature evolves over time. I recall (perhaps falsely) that The Cloud was specifically a term for elastic on-demand provider-managed compute/storage/network. Over time, it came to mean many other things. e.g. Salesforce Data Cloud.

I imagine if you step away from this for a year and come back, an agent will be something entirely different, perhaps a robotic horse, and a harness will be your saddle on the horse. Who knows?

[QuercusMax]

The Cloud originally just meant servers on someone else's network; it came from flowchart diagrams in the 70s.

[dd8601fn]

That’s basically how I always knew it. On a Visio diagram of your network, the thing on the other side of your router was literally a cloud.

So if someone asked where your CRM was, and you weren’t doing something local like Dynamics (…vomit), well that thing was “over here, in the cloud”.

[cucumber3732842]

I worked at a classic "cloud" providing company. We called "the fog". That was more descriptive of the seemingly non-deterministic nature of the overall system(s).

[tremon]

The harness isn't either of those; the harness is quite literally a harness, giving the model/agent sensors and actuators (aka "skills") to interact with its environment. Compare with e.g. the Power Loader from Aliens: https://www.deviantart.com/pynion/art/Aliens-Power-Loader-11...

The model is still the model, and the agent is still the user<->model interface.

[dd8601fn]

Funny. harness = skills is one I hadn’t even heard yet.

But given the wide variety of mutually exclusive answers here, maybe you can get away with that.

[Dylan16807]

Here's how I see it: "Agent" isn't really describing a component, it's describing how you use the LLM. You have the model, and you have a harness around it that might be minimal or might have more features. If it's directly responding to user actions then it's not an agent, if it's semi-autonomous then it's an agent. (Yes this line is sometimes fuzzy.)

[shafyy]

There are new buzz words every two months. Remeber yesterday when everbody was throwing around RAG?

[ACCount37]

RAG died to better AIs. Turns out that a sufficiently advanced agentic model can do more than what RAG does with nothing but a grep tool over a pile of text files.

[majormajor]

I think if the dream of semantic search from vector embeddings had worked out as well as people had hoped then "grep over a bunch of text" would have some significant disadvantages.

But in practice I never saw anyone crack the embedding-generation-and-comparison problems well enough to actually get better results than grep for things like "find similar code and see what it does."

(You also don't need that advanced a model to use "grep over a pile of files", but the models today can run MUCH faster than GPT 3.5/4 were running over the APIs back then, making "summarize all five hundred of these matches from those files" much more usable.)

[dd8601fn]

I’ve had very good luck having my system search for available tool functions with natural language (ultimately against Qdrant). I’m surprised to hear that people are trying to grep files, instead.

[ACCount37]

People? No, that's what AI agents themselves do.

There are theoretical gains from using a vector search engine in an agentic loop, but grep is the lowest common denominator of agentic search.