Artificial intelligence (AI) in simple terms refers to machines or software that can perform tasks we associate with human intelligence. This includes understanding language, recognizing patterns, learning from data, and making decisions.
AI enables computers to mimic human-like cognitive functions–learning, problem-solving, etc.--whether through hard-coded rules or by learning from examples. Spam filters, voice assistants, and recommendation systems all use AI techniques to work intelligently behind the scenes.
When we get into more detail about AI, we start to hear terms like “generative AI” and “agentic AI”. These are more nuanced definitions of types of AI that are worth understanding. At a basic level, you can think of generative AI as AI that creates content, and agentic AI as AI that takes action. But let’s get into a more detailed explanation of the two.
Generative AI and agentic AI serve different purposes in the AI world. In a nutshell, generative AI is about creating content, while agentic AI is about acting to achieve goals. One generates, the other operates. Let’s break this down in plain language.
These are AI systems that produce new content (text, images, audio, etc.) based on patterns learned from existing data. Given a prompt or input, a generative AI model can create a coherent response or design.
However, it only does what you ask it to do. It doesn’t actively make decisions beyond generating the next chunk of content. For example, if you ask a generative AI to “write a poem about sunrise,” it will write the poem, but it won’t decide on its own to also paint a picture of the sunrise or schedule an early-morning alarm at sunrise.
Generative AI Example: A chatbot like ChatGPT that generates answers to your questions, or an image model that creates artwork from a description.
Key Trait: Generative AI is reactive. It responds to a user’s prompt, within the predefined parameters. It doesn’t adapt on the fly or take further initiative. It’s constrained to what it learned during its training, and the specific task it was given.
These are AI systems designed to autonomously make decisions and take actions to reach a specific goal. An agentic AI doesn’t just generate information–it can act on information. It can plan a series of steps, invoke tools or software, and adjust its plan based on what happens, all with minimal human intervention.
Agentic AI Example: An AI agent that can plan your travel itinerary end-to-end–finding flights, comparing prices, and booking the best option–based on a goal you give it (“book me the cheapest 7-day flight to London next month”). Another example is a virtual assistant that can not only draft an email (generative) but also decide to send it, set a reminder in your calendar, or look up additional details, as needed.
Key trait: Agentic AI is proactive. It has a degree of agency (sometimes referred to as “autonomy” in AI spaces) in how it operates. Such an AI can take initiative, sequence multiple tasks, and adapt to new information in real time.
Unlike generative models that wait for a prompt, an agentic AI can say “Given my goal, I should do X next” without being explicitly told each step.
Not all “agentic” AIs are equally capable. Just as we talk about levels of autonomy for self-driving cars, AI researchers have defined levels of agency for AI systems. These range from simple automation to fully independent AI. A useful framework (inspired by one from Vellum.ai) breaks it down as follows:
At this level, there’s no true intelligence. The system only follows predefined rules or scripts (like the old rule-based systems or basic software macros).
It can’t learn or make decisions; it just executes fixed instructions. Think of a simple if-this-then-that script. It’s useful, but very brittle. If conditions change, it won’t know what to do.
Here we start seeing a tiny bit of AI. An L1 system can take an input and produce an appropriate output using learned patterns.
A classic Generative AI model falls in this category. It can answer questions or generate content based on what it learned, which is a form of intelligence. However, it has no memory of past interactions, no planning ability, and no initiative. It reacts to a prompt, and that’s it. Most current chatbots or AI assistants (without special augmentations) operate at this reactive level.
At L2, the AI can not only generate responses, but also use tools or take actions when needed.
This is where an AI stops being just a “content generator” and starts being an agent. An L2 agent can decide, for example, to call an API, do a web search, or consult a database to help fulfill a request. This AI is actually making decisions during its process.
Many advanced AI applications today aim for L2. The AI can do things like fetch information, run calculations, or invoke other services based on the prompt. This greatly extends what the AI can do, because it’s not limited to its trained knowledge.
However, it doesn’t set its own long-term goals, and doesn’t keep working indefinitely. There’s no persistent memory of context beyond the immediate task.
At L3 and beyond, agents become increasingly autonomous and continuous. The agent can plan multi-step workflows on its own and iterate on results.
It might monitor for certain events, maintain some memory of state, and adjust its plan as it goes. An L3 agent begins to look like an autonomous process that can handle more open-ended tasks, though still within some scope.
The L4 agent can operate indefinitely and adaptively. It maintains state over time, handles interruptions or new goals, and doesn’t need to shut down after one task. This is akin to an AI that could run on its own server, watching for opportunities to help you and acting without being prompted each time.
This is largely hypothetical for now. This is an agent that is not just autonomous, but can improve itself or create new solutions beyond its original programming. At L5, an AI could generate its own tools or adapt completely to novel problems on the fly–something we haven’t achieved in practice yet.
Today, most practical AI agents are around L1 or L2 in this spectrum. In fact, many of the AI systems businesses use are still essentially L1 (smart responders) with some clever programming around them. Levels L3+ exist, but are rarer and more experimental in real-world applications.
L2 is a sweet spot where we get useful autonomy (like automating multi-step workflows) without the unpredictability of a completely free-roaming AI.
Agent.ai is a marketplace designed to make it easy for people to create agentic AI solutions by leveraging the power of generative AI. In simple terms, Agent.ai provides the best of both worlds: You get the creativity and intelligence of generative models and the goal-driven autonomy of agentic systems.
Under the hood, Agent.ai uses large language models (LLMs) and other generative AI as the “brains” of the agent.
These models are excellent at understanding user input and producing coherent outputs. Agent.ai takes advantage of that. So when you build an agent, you don’t have to invent AI reasoning from scratch–you’re using state-of-the-art genAI as a foundation.
On top of the generative core, Agent.ai introduces an orchestration layer that enables reasoning and actions. This means an agent can break a goal into steps, call external tools or APIs, and make decisions iteratively.
For example, if you ask an Agent.ai agent to “analyze these sales figures and email me a summary,” the platform might use a generative model to interpret the request and produce a summary, and use agentic capabilities to fetch the data from a spreadsheet and send the email via an integration.
The agent does the whole task, not just one step of it.
A big advantage of Agent.ai is that it comes with many AI models and skills out-of-the-box. You can mix and match pre-existing models as building blocks for your agent, rather than training a new model for each task. This means you can focus on the logic of the agent (what it should do, in what order) and let the platform handle the AI heavy lifting.
Because Agent.ai supports a wide array of models, you can choose the right model for each sub-task–whether it’s a language task, a vision task, or a specialized domain–without needing to fine-tune your own AI model for each requirement. This flexibility accelerates development and experimentation.
By combining generative and agentic approaches, Agent.ai allows you to create task-oriented, goal-driven AI agents that are far more useful than a stand-alone chatbot. The generative part ensures the agent can understand context and produce complex outputs (like writing a report), while the agentic part ensures it can actually do things (like gathering needed information, executing commands, or interacting with other software).
As AI becomes more integrated in our work and products, we’ll increasingly rely on agentic AI to handle complex tasks–from customer service bots that can actually resolve issues, to software assistants that can automate entire workflows. Generative AI is a component in these systems, providing the brains and creativity; but without agency, it would always require a person in the loop for the actual doing. Agentic AI brings us closer to AI that doesn’t just inform or suggest, but can take action safely under our guidance.
If you’re excited by the idea of AI that can actually get things done, it’s a great time to explore Agent.ai. You don’t have to be a developer to build an agent. There are low-code and no-code agent building capabilities, or, you can try one of the pre-built agents to get your feet wet. Check out the agent marketplace, and dip your toes into the agentic pool. The water’s fine.