Introduction: The Two Faces of AI – Creation and Action
There are two main faces of AI: Generative and Agentic. Each paradigm fulfills a specific purpose. Generative AI is used to create. Agentic AI is used to act. When you’ve got an AI-powered customer service bot, it’s Agentic. When you ask ChatGPT for advice, conversation, images or interpretation, that’s Generative.
- Generative AI: Use AI to produce new data. Whether it’s a synthetic dataset, personalized chat or creating images and videos, Generative AI produces new content based on patterns learned from its pretraining.
- Agentic AI: In this paradigm, AI is used not to create, but to accomplish a task. These AIs are used to fill actual roles like customer service, automated workflows and autonomous robots.
Generative AI: The power of creation
Generative systems create new data based on patterns learned from existing material. These models don’t retrieve or remix the original training data. Generative models produce new, unique data — reflective of the original patterns. Your model writes text, paints an image or creates code. With enough training data, its output is different from the original training data.
At the heart of these generative systems is a massive neural network, the driving force behind ChatGPT, DALL-E and even Stable Diffusion. These models don’t exist to complete a task, but to create new content. Well-trained models often produce outputs that resemble and even rival human-created works.
In large-scale generative models training datasets, sources often include public web content, academic works and other sources of accessible data. This training data needs to be handled responsibly. When a model gets trained on highly specific content, sometimes it runs the risk of accidentally reproducing its training material.
To avoid accidental reproduction of training data, teams need to curate their data carefully and create safeguards to prevent leakage of the original training data. Responsible data sourcing and model evaluation hold the keys to ethical and trustworthy Generative AI.
This can be handled by building safeguards for your output. All output should pass through a filter that checks for hallucinations and duplicated content. If a response is inappropriate for one reason or another, this response should never reach the user. Instead, the model should retry the response — or explain that policy explicitly blocks it.
Generative AI learns patterns from raw data. It then uses these patterns to create new outputs that echo the original training material without copying it.
Agentic AI: The drive for autonomy
Agentic AI systems don’t create, they do the thing. Think of them as a super intelligent for loop. When you use an Agentic AI, your agent doesn’t create, it performs. You can think of your agent like a toaster that learned 99% of the world’s existing data so it could be the best toaster it can be. Sounds a bit absurd right?
As absurd as it sounds — this is a standard practice in modern AI engineering. Engineers will often connect a generative model (like ChatGPT) to external tools like an MCP server. Then, it uses these tools to perform all sorts of actions. One AI Agent might harvest web data. Another might run your washing machine with custom settings — lights or darks, cold or hot.
While these models often share a training foundation with Generative AI, their training powers decision making — not content creation. Agentic AIs rely on real-time data. If your agent controls the washing machine, it needs to know which clothes it’s washing. If your agent harvests web data, it needs to be able to see the page — in a real browser environment.
Without real-time data, these agents can’t make informed decisions. Imagine a stock trader bot that decides based on yesterday’s prices. Imagine a navigation system that plans your route using traffic data from 15 years ago.
Agentic AI relies on live data. This data is used for all sorts of purposes from laundry and logistics to web crawlers and Teslas.
Key differences: Generative AI vs. Agentic AI
| Feature | Generative AI | Agentic AI |
|---|---|---|
| Core Purpose | Create new content | Complete tasks or achieve goals |
| Output | Text, images, audio, video, code | Actions, decisions, tool usage |
| Data Usage | Trained on large static datasets | Uses real-time data from environment or tools |
| Interaction Type | One-shot or session-based generation | Continuous perception-action loop |
| Examples | ChatGPT, DALL·E, Stable Diffusion, MidJounrey, ElevenLabs | AutoGPT, LangChain agents, AI-powered workflows |
| Tool Usage | Rarely uses tools (mostly standalone) | Often uses tools, APIs, web browsers, MCP |
| Training Focus | Pattern recognition and replication | Goal-driven decision making |
| Dependency on Web | Uses web for training data | Uses web as a live environment or resource |
How web data fuels both paradigms
Generative AI
Generative AI uses web data for training purposes. Almost all meaningful AI training data comes from the web. You don’t need a computer science degree to know that the internet is the largest dataset ever created. It is host to a vast collection of archives containing almost the entirety of documented human history. In simple terms, the internet has almost everything. The most efficient way to acquire AI training data is through the internet.
Web data encompasses just about everything. For an LLM for understand text conversations, books and social media are a goldmine. To understand a little bit about everything, Wikipedia gives it a summary of most human knowledge. To understand images and videos, you can make use of public data sources from everywhere.
Prioritize sourcing data that is publicly accessible, these should be your default sources of training data. Note that public accessibility does not remove potential copyright restrictions. It is on the user to ensure that the data sources and their use comply with all applicable legal and licensing requirements for their application and jurisdiction.
Web data creates the foundation for Generative AI. Without web data, generative models wouldn’t know what to generate.
Agentic AI
Agentic AI doesn’t read the internet, it interacts with it. These bots need live data for real time decisions. Five years ago, the internet was full of ‘dumb’ bots. Today, we have even more bots and these bots are now powered by AI.
Agentic AI needs to react to new information. Think of a trading bot for example. Sure, historical training is useful for the bot to recognize patterns. The bot needs the current price of Bitcoin to make informed trades.
Agentic AI treats the internet like an environment, not a library. These bots need to interact with pages, tools, APIs and often CAPTCHAs. Your model’s output isn’t based on training data. Your model performs based on what it sees.
These agents manipulate the web. Agentic systems often chain together tools combining scraping, API calls and form submission. Whether you’re trading crypto or handling irate customers, your AI model needs the live data — or your customers might do business elsewhere.
An AI agent might scrape a product page and analyze revie sentiment. Then it might weigh products against your current inventory and place an order. All of this can be done without human intervention.
Agentic AI doesn’t read the web, it often lives on it.
Gen AI and Agentic AI use cases
Generative AI
- Content Creation: Craft blog posts, social media content, images videos and more.
- Text Summarization: Read over large portions of difficult text and distill them into something for everyday humans.
- Code Generation: Software jobs that used to take weeks are now finished in minutes. You no longer need to spend 40 minutes writing a file with unit tests. An LLM can give you both the file and the unit tests in about 4 seconds.
- Synthetic Data: Synthetic data is often used to preserve privacy. A model might analyze a customer database to reveal real patterns. Then, it can generate a new database that reflects those patterns without revealing the actual customers.
Agentic AI
- Web Automation: Automate workflows on the web. These models can now handle nearly the entire Extract Transfer Load (ETL) pipeline. They can scrape (Extract) the data. Then they can form it to your system (Transform). Finally, send it through your pipeline (Load).
- Trading Bots: React in real time with trading strategies defined through Natural Language Processing (NLP).
- Customer Support: File help desk tickets, answer queries and react based on sentiment.
- Scheduling and Booking: Book flights and hotel rooms with natural language.
- Smarthomes and Appliances: Dim your lights or wash your clothes with just a few words.
The convergence and future of AI
The barrier between these two paradigms is fading away completely. Agents often write code on the fly. They create prompts and even launch new agents. In many cases, AI agents are already navigating customer service and social media with near autonomy. When an agent replies to a post or files a support ticket, it often combines Generative and Agentic principles.
We are moving toward systems that not only generate content, but also evaluate it and decide what to do next. Agents creating other agents with no human involvement whatsoever. In the near future, economies will shift. Even today, ideas become something tangible within just minutes. Work that used to take weeks is now done in an hour.
As both Agentic AI and Generative AI continue to evolve, the more they overlap. Soon, all AI will be Agentic and Generative — it’s already happening now.
Implications for web data strategies
Web data isn’t just fuel anymore, it’s infrastructure. Generative AI needs web data in order to learn. Agentic AI needs web data in order to function.
- Generative AI: Use clean, well labeled datasets reflective of your desired learning patterns. Scrape the web, then transform it into something easily digestible.
- Agentic AI: Use web scrapers, APIs and other pipelines with structured real time output.
The convergence of these two paradigms is reshaping whole industries. Take any modern AI chatbot as an example. Think of ChatGPT, Gemini, Grok, Claude or any other model people currently use as a daily driver. These models are able to perform web searches (Agentic) and generate content (Generative). This convergence is happening faster than most can comprehend.
Your data stack determines what your models become. When you design your system, you need to be mindful of both quality and adpability. Striking the proper balance unlocks the best performance when using models and agents.
Understanding the strengths of creation and action in AI
AI is no longer a single paradigm. AI doesn’t just chat. It doesn’t just create or accomplish. Generative and Agentic AI are converging to a point of real autonomy.
If you can combine these two paradigms, you’re already at a tremendous advantage. The future belongs to those who master both.