Artificial intelligence has changed a lot over the years, think of pseudo-ai like Siri and Alexa, you played songs or alarms. They were useful to some extent but very limited. You had to ask them to do something, and they would. It was like talking to a vocal bot that only knew a few answers.
Then, tools like Chatgpt where companies began to use them for writing email, reports or easily responds to customer concerns. For example, a marketing team could ask to write a campaign plan, or an assistance team could use it to repair automatically to frequent requests. But they still needed you to tell them what to do. Imagine having an assistant who only speaks when he is spoken. It would not take any kind of initiative. He will wait for you to order, he will do the job, and that’s it. It was a step forward, but still not a real partner.
The AI landscape is divided into two layers: foundation manufacturers and application innovators. Giants like Openai, Anthropic, Google, Meta and Deepseek run to develop large language models (LLM) which is the brain behind AI, models that are not designed to act independently.
Startups, however, take these models and transform them into action -oriented agents. By building fundamental LLM, they create an AI that does not speak only but it is. This is where we move more, at a time of agentic AI. It’s different. The agentic AI does not only wait for all the instructions, mainly it will seek a final objective. He may think, make decisions and act alone.
But how does that help? And how can businesses use this? This article will explain why the agent IA is more than another AI tool and will examine how it solves real problems when using it, and what it means for the future of work with actual examples of use in the industry. Today, Chatgpt or AI models need clear prompts. The agentic AI goes further by understanding the tasks at a higher level, planning the steps and performing them with a minimum of human intervention. Agents are a necessity, companies that use it will have a major advantage, it can automate workflows, improve efficiency and reduce costs. It is not only a question of replacing human work, but improving what people can really do while AI manages repetitive tasks. For EG, customer service teams spend hours responding to the same types of requests. With the operator of agentic AI like OpenAi, these tasks can be fully automated, the AI not only responding, but understanding of the problems of intention and resolution in itself.
Why the agent IA has:
Example of intelligent work against hard work has become real. The myth that human effort alone stimulates success. Startups that cling to the old workflows will disappear, those who use technology will keep a major skin in the game by allowing machines to manage execution while humans focus on vision. Agent AI startups are a new trend in Silicon Valley, increasing millions for a reason, they build the “last mile” of automation. While giants like Openai focus on language models, startups transform these models into autonomous agents that act in the real world. While everyone is obsessed with chatbots, the agent has quietly helps the boring sectors such as agriculture where startups like Taranis use AI agents to analyze soil data, predict pest epidemics and automatic order pesticides. The agentic AI does not consist in replacing humans, it is a question of redefining what humans do, the world becomes faster, more disorderly and more unpredictable.
There was a false idea on AI agents and AI assistants. Take an example of Siri or Alexa, they help you with tasks when you ask. While an AI agent works more alone. He can do things for you without you having to wonder each time. For example, he could look for an online business and use this information to answer your questions. Recently, the Openai operator can use the browser to perform the actions. However, there is overlapping between an assistant and an agent, for example, if you ask an assistant to find the best pasta recipe, he could search for the web and give you an organized response, acting both as assistant and agent.
How it works:
Let us compare how traditional AI (like Chatgpt) and the AI agent deal with a user’s request: “I’m going to Boston next week. Advise what I should bring with me.
Cat based on a GPT prompt -> you ask, and it responds once. “Boston weather next week is 50 to 65 ° F with rain on Tuesday. Drats pack, a waterproof jacket, comfortable shoes and an umbrella. »»
Boundaries:
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Static response. No follow -up unless you ask again.
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Is based on existing data (for example, the weather at the time of the request).
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Unable to check updates in real time or your personal preferences.
AI agent -> in a loop -> “I’m going to Boston next week. Find me hotels with a free breakfast and repayable rates. »»
Step 1: The agency AI identifies fundamental objectives: find hotels in Boston and filter for free breakfast and repayable rates.
Step 2: Open a browser uses tools like Puppeteer / Selenium to access the booking sites. Look for Boston hotels and check the equipment. Eliminates hotels that do not meet the criteria.
Step 3: If the first site has limited options, it goes to the following platform.
Step 4: Offer the hotel results A: $ 200 / night, free breakfast, free cancellation. Hotel B: $ 180 / night, free breakfast, repayable if you are canceled within 48 hours.
Step 5: Book the hotel if you get permission. He can always request a final confirmation, but has still saved a lot of time in research and find a hotel that corresponds to all the criteria consult the screenshot below for reference where the user provides personalized instructions to follow the criteria while looking for hotels.
The agentic AI works in a continuous, planning, acting, learning and adaptation loop until the end of a task.
Keep in mind that the AI agency is a tool, not a replacement. Use it to manage the “how” of tasks with clear rules and objectives. Avoid it for “why” or “who” decisions that need empathy, creativity or human judgment. Companies that balance this will win; Those who will not face a backlash.
Challenges with Aiatic IA:
While we are talking about the positive points of the agentic AI, we move the head towards negatives with an example of AI hotel agent release customers during a storm that could prioritize costs on security, resulting in reputation damage. Recently, American Airlines has created an automated check system and depending on the user contribution, it automatically encourages users to record hand luggage also free of charge, if someone gets out with the system, this can be a loss for the airline.
All companies like efficiency but they often ignore the consequences of automation. For EG, the subtle manipulation of human decision -making by AI is flowing, and companies think they have control, but while agency AI assumes more responsibilities, it will start to shape commercial strategies and question the choices of managers. When decision -making is outsourced to AI, human judgment becomes secondary. Companies could wake up one day realizing that they no longer understand logic behind their own strategies.
As agent AI systems are evolving, they will control access to resources, markets and even whole economies, which is a kind of data intoxication.
Final reflections:
Given that recent operator developments by OPENAI, which is more a browser automation which can sometimes be blocked, for example certain applications which require connect or have a detection of robots to their load balancer, requests will be blocked. A simple example is Gmail only, many solutions have been built around sending automatic emails, but it does not generally work, so above it, you have recaptcha which is designed to solve this type of problem. It is therefore too early to define the scope of an operator who cannot completely take out the manual system of the work, at least in the foreseeable future.
Certainly, there are more advantages than disadvantages and which are worth trying from a commercial point of view, I think that which will go there first will have a long -term game and major skin of the game, a lasting advantage and at first engine in the construction of one of the best products in history.
Recent trends seem to report to the construction of specialized AI vocal agents for different use cases. DADA collected $ 3 million for AI agents who reserve restaurant reservations via telephone calls. Fellow has collected $ 5 million for outgoing sales agents who have cold call prospects, pitch products and planning demos.
AI race is no longer limited to technology giants. With open source models like Olllama, Huggingface, Mistral and Falcon, you can manage LLM models locally founding without having to spend a lot of money in advance to get GPUs.
Start small, automation a workflow, measure the impact and the scale. The objective is not to replace the labor market is to allow them to focus on what should be their goal.