4 ms·

Can someone explain to me in simple terms what an agent is? Is for example Google’s crawl bot an agent? Is there a prominent successful agent that I could tes

• LPisGood 1y ago
  The way everyone is using the term lately is to refer to an LLM that can use one or more tools, calculators, search engines, etc
• bognition 1y ago
  Think of an agent as a standalone script or service. They have a single function take inputs and create outputs. You can chain agents together into a string to accomplish larger tasks. Think of everything involved in booking travel. You have set a budget, pick dates, chose a destinations, etc…. Each step can be defined as an agent and then you chain them together into a tool that handles the entire task for you.
• koakuma-chan 1y ago
  An agent in this context is simply an LLM that has tools.
  • XenophileJKO 1y ago
    There is an additional component. The LLM needs to determine when to use a tool and be capable of using more than one tool instance per logical task.
    • diggan 1y ago
      That's pretty much implicit when someone says "LLM that has tools" (what they mean between the lines is "A LLM that been trained to do tool calling, and used with a runner that can parse whatever tool calling/response format the model is trained for"), what would they refer to otherwise? Just that there is a list of tools but the LLM isn't even considering using them, or can only use one?
      • XenophileJKO 1y ago
        Certainly, for example I have created products that use tools, but in a workflow. It is common to give an LLM a tool or a few tools and make calling one of the tools the primary task of the prompt. Arranging these in a workflow to automate processes is common, but not agentic.
  • deleted 1y ago
    [deleted]
• thrance 1y ago
  A marketing buzzword for when you have multiple prompts.
• QuadmasterXLII 1y ago
  Agent originally meant an ai that made decisions to optimize some utility function. This was seen as a problem: we don’t know how to pick a good utility function or even how to point an ai at a specific utility function, so any agent that was smarter than us was as likely as not to turn us all into paperclips, or carve smiles into our faces, or some other grim outcome. With LLMs, this went through two phases of shittifaction: first, there was a window where the safety people were hopeful about LLMs because the weren’t agents, so everyone and their mother declared that they would create an agent out if an LLM explicitly because they heard it was dangerous. This pleased the VCs. Second, they failed to satisfy the original definition, so they changed the definition of agent to the thing that they made and declared victory. This pleased the VCs
  • Mordisquitos 1y ago
    In other words, VC-backed tech companies decided to weaken the definition of 'Torment Nexus' after they failed to create the Torment Nexus inspired by the classic sci-fi novel 'Don't Create the Torment Nexus'.
  • adastra22 1y ago
    "Agent" is a word with meaning that predates the LessWrong crowd. It is just an AI tool that performs actions to achieve its goal. That is all.
    • QuadmasterXLII 1y ago
      It had a meaning that predated the LessWrong crowd, but the LessWrong meaning had taken over pretty completely as of the GPT-4 paper, only to get swamped again by the new "agentic is good actually" wave. From the GPT-4 paper: """ 2.9 Potential for Risky Emergent Behaviors Novel capabilities often emerge in more powerful models.[61, 62] Some that are particularly concerning are the ability to create and act on long-term plans,[63] to accrue power and resources (“power- seeking”),[64] and to exhibit behavior that is increasingly “agentic.”[65] Agentic in this context does not intend to humanize language models or refer to sentience but rather refers to systems characterized by ability to, e.g., accomplish goals which may not have been concretely specified and 54 which have not appeared in training; focus on achieving specific, quantifiable objectives; and do long-term planning. Some evidence already exists of such emergent behavior in models.[66, 67, 65] For most possible objectives, the best plans involve auxiliary power-seeking actions because this is inherently useful for furthering the objectives and avoiding changes or threats to them.19[68, 69] More specifically, power-seeking is optimal for most reward functions and many types of agents;[70, 71, 72] and there is evidence that existing models can identify power-seeking as an instrumentally useful strategy.[29] We are thus particularly interested in evaluating power-seeking behavior due to the high risks it could present.[73, 74]"""
• adastra22 1y ago
  Claude Code / Cursor / Windsurf are agents. LLMs with tools.
• dragonwriter 1y ago
  An agent (in the way the term is commonly currently used around LLMs) is a combination of LLM, external tools, and management framework such that the total system can make (and make use of the results of) one or multiple multiple tool calls at the LLM direction without intervening user interaction to serve user needs. (Usually, in practice, this takes place in between the request and response in what is otherwise a typical chatbot-style interaction, though there are other possibilities.)
• deleted 1y ago
  [deleted]
• beacon294 1y ago
  An agent is a while loop.
• jeroenhd 1y ago
  An agent as far as I've seen people use it is a script that will add some stuff to your prompt and monitor the LLM's output for a specific pattern and execute code when it encounters that. For instance, you could have an "agent" that can read/edit files on your computer by adding something like "to read a file, issue the `read_file $path`" to your prompt, and whenever a line of LLM output that starts with `read_file` is finished, the script running on your computer will read that file, paste it into the prompt, and let the LLM continue its autocomplete-on-steroids. If you write enough tools and a complicated enough prompt, you end up with an LLM that can do stuff. By default, smart tools usually require user confirmation before actually doing stuff, but if you run the LLM in full agent mode, you trust the LLM not to do anything it shouldn't. curl2bash with LLMs, basically. An LLM with significant training and access to file access, HTTP(S) API access, and access to some OS APIs can do a lot of work for you if you prompt it right. My experience with Claude/Copilot/etc. is that 75% of the time, the LLM will fail to do what it should be doing without manually repairing its mistakes, but in the other 25% of the time it does look rather sci-fi-ish. With some tools you can tell your computer "take this directory, examine the EXIF data of each image, map the coordinates to the country and nearest town the picture was taken in, then make directories for each town and move the pictures to their corresponding locations". The LLM will type out shell commands (`ls /some/directory`), interpret the results as part of the prompt response that your computer sends back, and repeat that until its task has been completed. If you prepare a specific prompt and set of tools for the purpose of managing files, you could call that a "file management agent". Generally, this works best for things you can do by hand in a couple of minutes or maybe an hour if it's a big set of images, but something the computer can now probably take care of you for you. That said, you're basically spending enough CO2 to drive to the store and back, so until we get more energy efficient data centers I'm not too fond of using these tools for banal interactions like that.
• IncreasePosts 1y ago
  prompt = user_input() while prompt != "exit": prompt = replace_tool_calls_with_results(call_llm(prompt))