Recently someone asked me what exactly agentic AI is (I work for Agent.ai, so this question comes up). They knew what AI was, broadly speaking. They had used ChatGPT to help them come up with a kid-friendly recipe using the ingredients in their fridge. They played around with the Agent.ai meme generator. But what is agentic AI? And how does it differ from what we’re doing when we use AI to make up a bedtime story for our 4-year old that features a puppy, a capybara, and a pigeon that team up to fight crime?
Agentic AI is the next step beyond simple automation and content generation toward systems that can independently make decisions, solve complex, multi-step problems, and take action on their own. In other words, they have agency.
While generative AI – the use of AI to generate content – has received a tremendous amount of attention, agentic AI has the potential to revolutionize how we do our jobs, run our businesses, and achieve our goals.
While traditional AI systems follow strict rules to operate, and generative AI typically responds to prompts, agentic AI can operate with minimal human oversight. It’s capable of making good decisions and executing tasks independently–provided it’s been given enough context to operate in that environment.
In that way, it’s not much different from a great employee. With enough context, information, and the right guidance, a person (or an agent) can learn to make good decisions without your oversight.
This ability to act with agency is what separates agentic AI from other forms of AI. Let’s look at some of those distinguishing characteristics:
To better understand where agentic AI fits in the AI landscape, it's helpful to compare it with other forms of AI technology:
Traditional AI systems are designed to solve specific, predefined tasks with strict parameters. They follow programmed rules, and are good at repetitive tasks. However, they are not able to adapt to new situations or make autonomous decisions.
For example, a customer service chatbot using traditional AI might help customers navigate predefined issues, but becomes confused when faced with unexpected questions. At that point, it would refer the customer to a predefined destination for further assistance.
Generative AI uses large language models (LLMs) – one you might have heard a lot about is ChatGPT – to create content. That content might be text, images, or even code. How good your output is depends on how good your prompt is, so it is still largely reliant on the user and is unable to work autonomously.
For example, a generative AI system can write an email based on your request but typically can't decide when to send emails, to whom, or follow up on responses without explicit instructions. How good those emails are also depends on how much information you provide, since a generative AI system has no way of knowing the context around the email or the person receiving it without user input.
Agentic AI combines the generative capabilities of large language models, the precision of traditional programming, and adds the element of autonomous action. It can understand goals, develop plans to achieve them, and execute those plans by coordinating across multiple systems and data sources.
But … how exactly does agentic AI do that?
Agentic AI systems are typically composed of multiple AI components working together to achieve a goal. Here's how those components work together to do that:
A user provides some instructions or goals. For example, you might say "Research this prospect’s website and provide some recommendations for improving their site’s accessibility for our meeting.”
The AI agent then breaks down the goal into tasks and subtasks. Some tasks may be delegated to other agents with more specialized abilities.
The agent may need to access various tools and data sources to complete these tasks. For the website analysis example, it might search the web, access internal databases, and use data analysis tools to gather and process information.
Throughout the process, the AI agent checks its progress, adjusts its approach based on findings, and may interact with the user for clarification or confirmation, if needed.
The system completes the tasks and takes appropriate action—whether that's generating a report, making recommendations, or implementing changes in connected systems.
Because of the ability to take autonomous action, agentic AI offers present an exciting opportunity for people and businesses to streamline the parts of their work that do not require expertise, but do take up significant time. If you’re wondering where it might fit into your business, a few areas to consider may be:
Agentic AI can transform customer interactions by handling complex inquiries across multiple channels in a way typical chatbots are unable to manage. Agentic AI maintains context throughout extended conversations and is able to tap into multiple departments and systems in order to resolve issues, without transferring you to a new person to start from scratch to resolve a problem that may span multiple teams.
Traditional chatbots, on the other hand, are programmed to respond to a defined set of questions and will run up against limits of helpfulness quite quickly (as we’ve all probably experienced).
Agentic AI is excellent at automating complex workflows that require coordination across multiple tools and platforms. It can manage intricate decision trees with variable inputs and outputs, adapting it to complete specialized tasks. Tax and accounting professionals, for instance, could leverage agentic AI to autonomously gather client data, identify missed deductions, and prepare returns—all while adapting to changing regulations.
None of this removes the need for qualified accountants to review and validate the work done by AI, but it adds efficiency and surfaces value for clients that didn’t exist before.
For business leaders, agentic AI can serve as another source of research and decision support by analyzing data from multiple sources, identifying patterns, and providing actionable insights. The ability to synthesize information quickly and concisely provides another source for analysis and insight, but also goes beyond simple data analysis by suggesting concrete actions based on findings.
Agentic AI can function as an executive assistant that not only schedules meetings but also prepares meeting notes, follows up on action items, and prioritizes tasks based on your working patterns and personal goals.
Agentic AI represents a monumental shift in how people and businesses can make use of artificial intelligence to meet their goals. And we’re just at the beginning. While many have tinkered with AI, far fewer have harnessed the true power of agentic AI. Those that do, however, will enjoy greater collaboration between formerly disparate systems and teams, more sophisticated outputs, and perhaps most importantly, more free time to do the things people are best at.
The key is approaching agentic AI not just as another tool, but as a collaborative partner that can handle complex tasks independently while still aligning with human-defined goals and values.