How Agentic AI is Built on Large Language Models Like ChatGPT and Watsonx

Artificial Intelligence (AI) is rapidly evolving, and one of the most exciting advancements is the emergence of agentic AI—AI that can autonomously plan, act, and make decisions. These systems go beyond traditional chatbots and are built on top of powerful Large Language Models (LLMs) like ChatGPT and IBM’s watsonx.

In this blog post, we will explore:

• How agentic AI works.
• How LLMs like ChatGPT and Watsonx serve as the foundation for agentic AI.
• The role of external tools and real-time data retrieval in enhancing AI autonomy.

What is Agentic AI?

Unlike traditional AI, which reacts to user prompts, agentic AI takes initiative. It can:
✅ Break down complex problems into steps.
✅ Use external tools and real-time data.
✅ Adapt responses based on changing circumstances.

Imagine an AI that not only answers your questions but also searches the web, runs calculations, and integrates APIs to provide you with the most accurate and up-to-date responses. That’s the power of agentic AI!

How ChatGPT and Watsonx Enable Agentic AI

1. Core LLM Capabilities

At the heart of agentic AI lies a powerful LLM like ChatGPT or IBM’s watsonx, which provides:

• Natural language understanding to process human input.
• Pre-trained knowledge from vast datasets.
• Reasoning and contextual awareness to generate coherent responses.

2. The Agentic AI Layer

On top of the LLM, additional functionalities are integrated to give AI autonomy:

• Planning: The AI breaks down tasks into multiple steps rather than just responding to a single prompt.
• Decision-Making: It determines which tools to use (e.g., databases, APIs, search engines).
• Memory & Adaptation: It learns from previous interactions to improve future responses.

For example, IBM watsonx is specifically designed for enterprise AI solutions, allowing companies to train their own AI models, integrate them with existing business systems, and deploy them in a controlled and secure environment. This makes watsonx a powerful choice for enterprise-grade agentic AI.

How Agentic AI Uses External Tools

One of the biggest limitations of an LLM is that its knowledge is static—it doesn’t automatically update in real time. However, an agentic AI overcomes this by accessing external sources:

1. Real-Time Web Search

If an LLM lacks certain information, it can be programmed to search the web and fetch relevant data.

🔹 Example: A financial AI agent can analyze stock market trends in real-time rather than relying on outdated data.

2. API Integration

Agentic AI can connect to APIs for retrieving real-time weather, news, or customer data.

🔹 Example: A customer service AI can pull live order status from an e-commerce database.

3. Running Code for Complex Tasks

Agentic AI can execute Python scripts or other programs to solve problems beyond simple text-based reasoning.

🔹 Example: A scientific AI assistant can run statistical models to analyze trends and generate reports.

Watsonx vs. ChatGPT: Which is Better for Agentic AI?

👉 Watsonx is ideal for businesses that need enterprise AI with full customization and security.
👉 ChatGPT is more flexible for general AI applications but has limited enterprise-level customization.

The Future of Agentic AI

Agentic AI is set to revolutionize industries by making AI systems more autonomous, proactive, and intelligent. With LLMs like ChatGPT and IBM watsonx, businesses can develop AI solutions that go beyond answering questions—they can act as digital employees, automate workflows, and make data-driven decisions.

💡 As AI continues to evolve, we can expect more advanced integrations, multimodal capabilities (text, images, and video), and real-time decision-making in AI systems.

Would you like to implement agentic AI in your business? Explore IBM watsonx for enterprise AI solutions or use ChatGPT’s API for dynamic AI applications!

Final Thoughts

Agentic AI is the next step in AI evolution, and LLMs like ChatGPT and IBM watsonx are the foundation for these intelligent systems. Whether for business automation, customer support, or research, integrating agentic AI can lead to smarter, more efficient AI-driven solutions.