What do Salesforce, Google, OpenAI, Oracle, and ServiceNow have in common besides being tech giants? They’re all making a major shift from Generative AI to Agentic AI.
Agentic is all about agency—the ability to act independently, make choices, and influence outcomes. Think of it like an autonomous decision-maker that can take actions and control results on its own.
So, What Exactly Is Agentic AI?
Agentic AI refers to systems that can act autonomously, make data-driven decisions, and adapt over time. These AI models can interact with their environment—engaging with other agents, tools, and people—performing complex tasks without needing constant human input.
These systems can string together sequences of tasks to reach a goal, moving from thought to action and doing whatever it takes to get the job done. Of course, this opens up a whole new world of ethical questions, which we’ll cover into next.
LLMs vs. Agentic AI: What Are the Differences?
Imagine you’re using a Large Language Model (LLM) like ChatGPT. These models are trained on a vast mix of text from books, websites, and more. This broad training allows them to understand and generate human-like responses to a wide range of questions, even if those specific questions weren’t part of their training data. For example, you might ask about a very niche topic, and the LLM will provide an informed response based on the general knowledge it has absorbed.
Now, let’s look at Agentic AI. Think of it as an advanced version of LLMs. It uses similar foundational models but goes further. Agentic AI can adapt to new scenarios in ways LLMs can’t because it’s trained on a diverse range of unstructured data. This adaptability means it can handle complex tasks and workflows that it wasn’t explicitly trained on, just like LLMs handle unexpected questions.
Here’s how it works:
- LLMs: These models are trained to generate text and understand context. They excel at responding to prompts even if the exact scenario wasn’t covered in their training. Their strength lies in processing and generating language based on a broad spectrum of information.
- Agentic AI: This system builds on the principles of LLMs but adds the ability to take action based on its understanding. It’s designed to adapt to new situations and handle complex workflows. Even if a specific task wasn’t part of its training, Agentic AI can still manage it by leveraging its broad base of knowledge and advanced capabilities. You can direct Agentic AI using natural language, and it will execute tasks and processes as instructed.
LLMs and Agentic AI are game-changers. Unlike older systems that stuck to strict, predefined rules, these technologies use their broad, flexible training to tackle new and unexpected tasks on the fly. This adaptability is what sets them apart, making them way more powerful than the rigid models of the past.
How Agentic AI Perform Tasks Through Prompt Chaining
Prompt chaining is a technique in AI where multiple prompts are connected in sequence, with each prompt depending on the outcome of the previous one. It allows AI to break down complex tasks into smaller, manageable steps.
Imagine the user wants the AI to create, test, and deploy a Python function:
User Prompt: The user provides a high-level task (create, test, and deploy the function).
Step 1: The AI creates the function.
Step 2: The AI generates and runs unit tests to verify the function’s accuracy.
Step 3: The AI writes a deployment script for a cloud platform.
Step 4: The AI packages and deploys the function to the cloud (AWS Lambda).
Step 5: The AI tests the deployed function to confirm it works.
How Can Agentic AI Benefit Your Business?
Agentic AI offers huge value by tackling a wide range of tasks across different business areas. Unlike basic large language models, these agents can juggle multiple complex tasks at once, all on their own.
What really sets Agentic AI apart is its ability to work through natural language, making it super easy for anyone to give commands. Plus, they easily integrate with your existing tech stack, making them a seamless fit for your business.
Let’s take a closer look at the specific ways agentic AI can benefit enterprises:
1. Going from customer support to sales
Agents can handle typical tasks like answering customer questions, but they can go a step further. They can also identify sales opportunities. For instance, imagine a customer asking if a product has a specific feature. If it doesn’t, the agent can suggest an upgraded version or a complementary product, turning a support interaction into a sales opportunity. Additionally, agents can track customer preferences and offer personalized discounts or bundles, making the sales process smoother and more targeted.
2. IT on autopilot
These agents can automatically monitor your systems, apply updates, and even troubleshoot issues before they affect your business. Let’s say there’s a potential server outage—an agent can detect the early signs, reroute traffic, and notify the team, all without needing human intervention.
3. You never run out of stock again
If a popular product is running low, the agent can order more from the warehouse or suggest alternatives to customers while ensuring deliveries stay on time.
4. A new era in HR
Your new hire logs in on day one, and instead of dealing with a stack of paperwork or waiting for emails, ServiceNow Virtual Agent guides them through the process. It helps them complete forms, check off tasks, and get familiar with company policies—all from a single platform. This level of automation speeds up the onboarding process and enhances productivity right from day one.
5. Securing the cloud without human error
Larry Ellison, Oracle’s founder and chairman, has a clear stance on the rising threat of ransomware—humans are the number one cause of these breaches. By 2025, Oracle plans to move all applications to its autonomous database, aiming not just to save money, but to make data safer by eliminating human error. This shift means removing manual intervention in security tasks that often lead to vulnerabilities, like applying patches or updating configurations.
Ellison highlights that everything must be autonomous: “We will be off the older databases. Everything must be autonomous—not to save money, but to keep the data safer, with no human error.” Autonomous systems, powered by AI agents, can monitor cloud systems 24/7, automatically identifying threats, applying security patches, and managing configurations. These agents remove the human element from security, minimizing the risk of breaches due to mistakes.
Oracle is already using AI to manage much of its cloud infrastructure autonomously, stating that almost all databases in the Oracle Cloud are now autonomous. The ultimate goal? To automate every data center to ensure high performance, low cost, and enhanced security.
We’re at a turning point where major companies are racing to make agentic AI a standard in every office worldwide—just like how chatbots are now everywhere.
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