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Artificial intelligence has evolved significantly over the years. In the early days, we had pseudo-AIs like Siri and Alexa -- handy tools for playing songs, setting alarms, or answering basic questions. However, their functionality was limited. They operated as reactive voice bots, only responding to direct commands without any real autonomy or initiative.
Then came AI-powered assistants like ChatGPT, which businesses quickly adopted for drafting emails, reports, and handling customer inquiries. A marketing team could request a campaign plan, or a support team could automate responses to common questions. While this was a step forward, these tools still required human input to function -- they didn't act on their own. Imagine an assistant that only speaks when spoken to. It executes tasks efficiently but lacks independent thought or initiative.
Today, the AI landscape is divided into two key layers: foundation builders and application innovators. Tech giants like OpenAI, Anthropic, Google, Meta, and DeepSeek are competing to build the foundation layer by developing large language models (LLMs) -- the core intelligence behind AI.
Startups, on the other hand, are driving the next wave of AI by transforming these foundational LLMs into action-oriented agents. Instead of merely responding to commands, this new generation of AI -- often referred to as agentic AI -- is designed to operate with greater independence. It doesn't just wait for instructions; it identifies goals, makes decisions, and takes proactive steps to achieve them.
This shift marks a significant leap forward. We are now entering an era where AI moves beyond passive assistance to become a true partner -- one that not only understands and processes information but also thinks, plans, and acts autonomously to drive meaningful outcomes.
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 them will have a major edge. They 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 example, 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 versus 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 automate pesticide ordering.
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 them 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, you 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.
There's often confusion between AI agents and AI assistants. Assistants like Siri or Alexa perform tasks only when prompted, whereas AI agents operate more independently, proactively gathering information and executing actions without constant user input. While there may be some overlap, AI agents go beyond by autonomously handling more complex workflows.
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
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
* Static response. No follow-up unless you ask again.
* Relies on existing data (e.g., weather at the time of query).
* Can't check real-time updates or your personal preferences.
Loop-Based
Agent AI works in a loop until the task is finished or requires final confirmation.
"I'm going to Boston next week. Find and book a hotel room near the XYZ address that offers free breakfast and has refundable rates."
Look at the screenshot below for reference, where the user is providing custom instructions to follow the criteria while looking for hotels.
Implementing Agents Using OpenAI
Below is the sample code that shows how AI agents can be set up:
Creating a tool/agent that will be called during runtime:
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 about the 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 no 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 example, 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
With the recent development of OpenAI's Operator, which primarily functions as a browser automation tool, there are limitations to consider. It can be blocked in certain cases, such as when applications require login authentication or have bot detection mechanisms at their load balancers. For instance, Gmail presents a challenge -- while numerous solutions exist for automating email sending, they often fail due to security measures like CAPTCHA, designed specifically to prevent such automation. Given these constraints, it is still too early to determine the full potential of Operator, and it is unlikely to completely replace manual processes in the foreseeable future.
Definitely, there are more pros than cons, and it is worth trying out from a business point of view. I think whoever goes there first will have long-term play and major skin in the game, durability, and a first-mover advantage in building one of the finest products in history.
Recent trends seem to be signaling a shift 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.
