Artificial intelligence has changed a lot over the years think of pseudo AIs like Siri and Alexa, you used to play songs or set alarms. They were helpful to some extent but very limited. You had to ask them to do something, and they would do it. It was like talking to a voice bot who only knew a few answers.
Then came tools like ChatGPT where businesses started using them for drafting emails, reports, or addressing customer concerns easily. For example, a marketing team could ask to write a campaign plan, or a support team could use it to auto-reply to frequent queries. But they still needed you to tell them what to do. Imagine having an assistant who only speaks when spoken to. It wouldn’t take any kind of initiative. It will wait for you to command, it will do the job, and that’s it. It was a step forward, but still not a true partner.
AI landscape is split into two layers: foundation builders and application innovators. Giants like OpenAI, Anthropic, Google, Meta, and Deepseek are racing to develop large language models (LLMs) which is the brains behind AI, the models that aren’t designed to act autonomously.
Startups, however, are taking these models and turning them into action-oriented agents. By building on top of foundational LLMs, they are creating AI that doesn’t just talk but it does. That’s where we are advancing further, in an era of agentic AI. This is different. Agentic AI doesn’t just wait for all instructions, mainly it will look for an end goal. It can think, make decisions, and take action on its own.
But how does it help? And how can businesses use this? This article will explain why agentic AI is more than just another AI tool and look at how it solves real problems when to use it, and what it means for the future of work with some real usage examples in the industry. Today ChatGPT or AI models need clear prompts. Agentic AI takes it further by understanding tasks at a higher level, planning steps, and executing them with minimal human intervention. Agents are a necessity, companies that use it will have a major edge, it can automate workflows, improve efficiency, and reduce costs. It is not just about replacing human work, but about enhancing what people actually can do while AI handles repetitive tasks. For e.g. Customer service teams spend hours responding to the same types of queries. with agentic AI like OpenAI operator, these tasks can be fully automated, with the AI not just replying but understanding intent and resolving issues on its own.
Why agentic AI matters:
Smart work vs hard work example just got real. The myth that human effort alone drives success. Startups that cling to old workflows will fade, those using technology will keep major skin in the game by letting machines handle execution while humans focus on vision. Agentic AI startups are a new trend in Silicon Valley, raising millions for one reason, they’re building the “last mile” of automation. While giants like OpenAI focus on language models, startups are turning those models into autonomous agents that act in the real world. While everyone obsesses over chatbots, agentic AI is quietly helping boring sectors like agriculture where startups like Taranis use AI agents to analyze soil data, predict pest outbreaks, and auto-order pesticides. Agentic AI is not about replacing humans, it’s about redefining what humans do, the world is getting faster, messier, and more unpredictable.
There’s been a misconception about AI agents and AI assistants. Take an example of siri or alexa, they help you with tasks when you ask it to. While an AI agent works more on its own. It can do things for you without you having to ask each time. for example, it might research a company online and use that information to answer your questions. Recently, OpenAI’s operator can use the browser to perform the actions. Though, there is some overlap between an assistant and an agent, for e.g., if you ask an assistant to find the best pasta recipe, it might search the web and give you a curated answer, acting both as an assistant and an agent.
How it works:
Let’s compare how traditional AI (like ChatGPT) and Agentic AI handle a user’s request: “I’m going to Boston next week. Advise what I should bring with me.”
Prompt-based Chat GPT -> You ask, and it responds once. “Boston’s weather next week is 50–65°F with rain on Tuesday. Pack layers, a waterproof jacket, comfortable shoes, and an umbrella.”
Limitations:
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Static response. No follow-up unless you ask again.
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Relies on existing data (e.g., weather at the time of query).
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Can’t check real-time updates or your personal preferences.
Loop-based Agentic AI -> “I’m going to Boston next week. Find me hotels with free breakfast and refundable rates.”
Step 1: Agentic AI identifies the core objectives: Find hotels in Boston and filter for free breakfast and refundable rates.
Step 2: Open a browser Uses tools like Puppeteer/Selenium to access booking sites. Look for Boston hotels, and check amenities. Eliminates hotels that don’t meet criteria.
Step 3: If the first site has limited options, it moves to the next platform.
Step 4: Deliver outcome Hotel A: $200/night, free breakfast, free cancellation. Hotel B: $180/night, free breakfast, refundable if cancelled within 48 hours.
Step 5: Book the hotel if given permission. It may still ask for final confirmation but still saved lots of time in doing research and find a hotel that matches all the criteria Look at the below screenshot for reference where the user is providing custom instructions to follow criteria while looking for hotels.
Agentic AI operates in a continuous loop, planning, acting, learning, and adapting until a task is completed.
Keep in mind, Agentic AI is a tool, not a replacement. Use it to handle the “how” of tasks with clear rules and goals. Avoid it for the “why” or “who” decisions that need empathy, creativity, or human judgment. Businesses that balance this will win; those that don’t will face backlash.
Challenges with agentic AI:
As we spoke about the positives of Agentic AI, let us move our heads towards negatives with an example of an AI hotel agent rebooking guests during a storm that might prioritize cost over safety, leading to reputational damage. Recently American Airlines has created a system of automated check-ins and based on user input, it automatically prompts users to check in carry-on baggage also at free of charge, if someone gets away with the system, it may be a loss to the airline.
All businesses love efficiency but they often ignore the consequences of automation. For e.g. AI’s subtle manipulation of human decision-making is sinking, and businesses think they are in control, but as agentic AI takes on more responsibilities, it will start shaping business strategies and questioning leaders’ choices. When decision-making is outsourced to AI, human judgment becomes secondary. Businesses might wake up one day realizing they no longer understand the logic behind their own strategies.
As agentic AI systems evolve, they will control access to resources, markets, and even entire economies which is a kind of data poisoning.
Final thoughts:
Since the recent developments of Operator by OpenAI, which is more of a browser automation which can be blocked at times, e.g. certain apps that require log in, or have robots detection at their load balancer, the requests will be blocked. A simple example is gmail only, numerous solutions have been built around sending automatic emails but it generally doesn’t work, so on top of it you have reCAPTCHA which is built to tackle this type of problem. So it is too early to define the scope of an operator which may not completely get the manual system out of the work, at least for the foreseeable future.
Definitely, there are more pros than cons and worth trying out from a business point of view, I think whoever is going to go there first will have long-term play and major skin in the game, durable, and first mover advantage in building one of the finest products in history.
Recent trends seem to be signalling towards building specialized AI Voice Agents for different use-cases. Dada raised $3M for AI agents that book restaurant reservations via phone calls. Fellow raised $5M for outbound sales agents that cold-call prospects, pitch products, and schedule demos.
The AI race is not just limited to tech giants any longer. With Open-source models like Ollama, Huggingface, Mistral, and Falcon you can run foundation LLM models locally without needing to spend lots of money upfront to procure GPUs.
Start small, automate one workflow, measure the impact, and scale. The goal isn’t to replace the workforce, it is to empower them to focus on what should be their focus.
