{"id":215273,"date":"2025-06-26T08:34:02","date_gmt":"2025-06-26T06:34:02","guid":{"rendered":"https:\/\/www.aivancity.ai\/blog\/?p=215273"},"modified":"2025-06-26T11:33:33","modified_gmt":"2025-06-26T09:33:33","slug":"why-is-everyone-talking-about-agents","status":"publish","type":"post","link":"https:\/\/aivancity.ai\/blog\/why-is-everyone-talking-about-agents\/","title":{"rendered":"Why is everyone talking about agents ?"},"content":{"rendered":"\n

By Dr. Burak \u00c7IVITCIO\u011eLU<\/a> <\/strong>Associate Professor at aivancity<\/strong> in Artificial Intelligence, Machine Learning & Deep Learning <\/p>\n\n\n\n

Since the start of the AI revolution \u2014 let\u2019s call that the release of ChatGPT-3 \u2014 the capabilities of large language models (LLMs) have been accelerating rapidly.<\/p>\n\n\n\n

Let\u2019s put this into perspective :<\/p>\n\n\n\n

Just a few months ago, Veo 3 didn\u2019t exist \u2014 and now, it\u2019s setting new benchmarks in video generation.
A little further back, we were just beginning to see improved reasoning with Claude Sonnet 3.5 and GPT-4o.
Today, GPT-4o is the baseline<\/em>. We use it for quick responses, not necessarily deep reasoning.<\/p>\n\n\n\n

Since December 2024, we\u2019ve entered what is now called the era of reasoning models<\/strong>. It began with o1, which has already been replaced. Now, we have access to o3<\/strong>, OpenAI\u2019s most advanced reasoning model \u2014 as of today<\/em>.<\/p>\n\n\n\n

And yet, somehow, it already feels like these tools have always been with us. It\u2019s hard to believe ChatGPT launched just 2.5 years ago.<\/p>\n\n\n\n

What About the Cost of Intelligence ?<\/h2>\n\n\n\n

It\u2019s not enough to talk about smarter models without talking about how cheaper<\/em> they\u2019ve become. Let\u2019s get specific.<\/p>\n\n\n\n

LLM pricing is typically measured in price per one million tokens<\/strong>.
A token is basically a chunk of a word.<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n\n

<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n\n\n\n

    \n
  • \u201cLearning,\u201d for example, contains about 2 tokens.<\/li>\n\n\n\n
  • A standard A4 page has around 600 tokens.<\/li>\n\n\n\n
  • A typical novel? Roughly 100,000 tokens.<\/li>\n\n\n\n
  • So, 10 novels is around 1 million tokens.<\/li>\n<\/ul>\n\n\n\n

    Now let us pause for a second. Can you guess what 1 million tokens might cost today, and what they cost when ChatGPT was first released?<\/p>\n\n\n\n

    Here\u2019s the reality :<\/p>\n\n\n\n

      \n
    • In 2020, GPT-3\u2019s beta launch came in at $50 per million tokens<\/strong>.<\/li>\n\n\n\n
    • Today, GPT-o3 \u2014 the most advanced model available \u2014 is just $2 per million tokens<\/strong>.<\/li>\n\n\n\n
    • And GPT-4.1-nano, a faster and cheaper model, comes in at just $0.10 per million tokens<\/strong> \u2014 that\u2019s 500x cheaper than the original GPT-3<\/em>.<\/li>\n<\/ul>\n\n\n\n

      Worth noting: the cheapest model today is still<\/em> more capable than GPT-3 in 2020.<\/p>\n\n\n

      \n
      \"\"<\/figure>\n<\/div>\n\n\n

      This massive price drop changed everything. It unlocked access for researchers, developers, and hobbyists. Some models, like Mistral, are free for educational or personal use under the right conditions.<\/p>\n\n\n\n

      But how does this relate to AI Agents ?<\/p>\n\n\n\n

      What is an AI Agent ?<\/h2>\n\n\n\n

      OpenAI defines it simply:<\/p>\n\n\n\n

      \u201cAgents are systems that independently accomplish tasks on your behalf\u201d<\/p>\n\n\n\n

      Here\u2019s how that works in practice :<\/p>\n\n\n\n

        \n
      1. Task<\/strong>: The agent receives a natural language task.<\/li>\n\n\n\n
      2. Plan<\/strong>: It breaks that task down into subtasks using LLMs.<\/li>\n\n\n\n
      3. Tools<\/strong>: It executes the plan using tools \u2014 like browsing the web, running a script, or querying a database.<\/li>\n<\/ol>\n\n\n\n

        Let\u2019s say you ask an agent to \u201cfind information about Aivancity School of AI and Data for Business and Society, its ranking and educational quality.\u201d
        Here\u2019s what happens:<\/p>\n\n\n\n

          \n
        • The agent interprets the request.<\/li>\n\n\n\n
        • It builds a plan, maybe starting with search queries, followed by summarizing articles, then fact-checking.<\/li>\n\n\n\n
        • It uses tools like a browser to execute.<\/li>\n<\/ul>\n\n\n
          \n
          \"\"<\/figure>\n<\/div>\n\n\n

          As a result, the agent will find that aivancity is 1st<\/sup> in France according to Eduniversal rankings of schools of AI and Data Science.<\/p>\n\n\n\n

          So you can think of an AI agent as having three main pillars<\/strong>:<\/p>\n\n\n\n

            \n
          • A task<\/strong> to complete<\/li>\n\n\n\n
          • A plan<\/strong> to follow<\/li>\n\n\n\n
          • A set of tools<\/strong> to use<\/li>\n<\/ul>\n\n\n
            \n
            \"\"<\/figure>\n<\/div>\n\n\n

            Behind the scenes, all of this is made possible by LLMs: understanding what you say, deciding what to do, and executing with awareness of tool limitations and capabilities.<\/p>\n\n\n\n

            Why Is This a Big Deal ?<\/h2>\n\n\n\n

            AI Agents have existed as a concept for a while. What\u2019s changed is the economics, and the rapid performance increase of LLMs<\/strong>.<\/p>\n\n\n\n

            Thanks to lower costs and better models, we are no longer just generating text. We\u2019re generating outcomes<\/strong>.<\/p>\n\n\n\n

            That means we can get slides from a lecture video, organized notes from raw transcripts, booking flights, summarizing emails by urgency or importance; all using AI Agents.<\/p>\n\n\n\n

            In other words, LLMs are now becoming doers<\/strong>.<\/p>\n\n\n\n

            And that is why everyone is talking about Agentic AI.<\/p>\n\n\n\n

            <\/p>\n","protected":false},"excerpt":{"rendered":"

            Since the start of the AI revolution \u2014 let\u2019s call that the release of ChatGPT-3 \u2014 the capabilities of large language models (LLMs) have been accelerating rapidly.
            \nLet\u2019s put this into perspective<\/p>\n","protected":false},"author":3,"featured_media":215279,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[28],"tags":[],"class_list":{"0":"post-215273","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-articles"},"yoast_head":"\nWhy is everyone talking about agents ? - aivancity blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.aivancity.ai\/blog\/why-is-everyone-talking-about-agents\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why is everyone talking about agents ? - aivancity blog\" \/>\n<meta property=\"og:description\" content=\"Since the start of the AI revolution \u2014 let\u2019s call that the release of ChatGPT-3 \u2014 the capabilities of large language models (LLMs) have been accelerating rapidly. 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