Gen AI vs Machine Learning for Agentic AI

One afternoon at Bridgera’s office in North Carolina, a group of engineers gathered around their screens. They were working on something exciting – an AI agent named JERA. JERA could ingest large amounts of business data and train itself through a structured knowledge base. But JERA wasn’t just another chatbot. It could read documents, answer questions, make decisions, and even take actions like sending alerts or helping fix problems. It was something smarter and more powerful. It was an Agentic AI. 

Let’s explore how this new kind of AI works, how it’s different from regular machine learning and generative AI, and how it is changing how businesses operate. 

What is Artificial Intelligence?

Artificial Intelligence (AI) means creating computer programs that can think and act like humans. These programs can understand language, solve problems, recognize patterns, and even make decisions. 

In the past, AI systems used rules written by programmers. But today’s AI can learn from data and improve on its own over time. 

What is Machine Learning?

Machine Learning (ML) is a type of AI where the computer learns from data instead of following fixed instructions. 

For example, if you give a machine learning model a lot of information about past sales, it can predict future sales. As you feed it more data, it gets better at making those predictions. 

There are three main types of ML: 

• Supervised Learning: Learning from labeled examples (e.g., emails marked as spam or not spam).
• Unsupervised Learning: Finding patterns in data without labels (e.g., grouping customers by their behavior).
• Reinforcement Learning: Learning through trial and error, like how a robot learns to walk.

What is Generative AI?

Generative AI (Gen AI) creates new content. It can write text, draw pictures, generate code, and more. These systems use Large Language Models (LLMs) like GPT, Claude, or Gemini. 

For example, Gen AI can: 

• Write an article
• Summarize a report
• Translate a document
• Generate a recipe from ingredients

Gen AI doesn’t just analyze data. It creates new things based on what it has learned. 

How Are AI, Machine Learning, and Gen AI Connected?

Think of AI as the big umbrella. Under it, there are many types: 

• Machine Learning is how AI learns from data.
• Generative AI is a special kind of ML that can create content.

So, Gen AI is built using machine learning, and both are parts of the larger AI world. 

                                                                        Image source: PW Skills

What is Agentic AI?

Agentic AI is the next step in AI evolution. It doesn’t just give answers – it acts. 

An Agentic AI system understands tasks, makes decisions, and carries them out without needing constant human help or prompting. 

Let’s say you want a weekly report: 

• A Gen AI tool might write the report if you give it all the data.
• An Agentic AI will gather the data, understand what matters, write the report, and email it to your team, all by itself.

That’s the key difference: Agentic AI doesn’t wait for commands. It can plan and act on your behalf. 

Gen AI vs Machine Learning: Which Powers Agentic AI?

Agentic AI relies on both Machine Learning and Generative AI, each playing a unique role. 

• Machine Learning gives the agent the ability to learn from data. It helps the agent recognize patterns, make predictions, and personalize decisions. For example, an agent can learn a user’s preferences or detect when a system behaves abnormally.
• Generative AI enables the agent to understand and generate human-like language. It allows the agent to explain things clearly, summarize documents, or write emails and reports.

Together, these technologies power the thinking and communication behind Agentic AI: 

• ML brings learning and awareness
• Gen AI adds language and reasoning

Agentic AI needs both. It learns from data like an ML system and communicates and acts like a Gen AI system. Add in planning, tools, and memory, and you have an intelligent agent that doesn’t just respond—it takes action. 

It’s not about choosing between Gen AI or Machine Learning. It’s about how they come together to build agents that think and do. 

Why Are Businesses Excited About Agentic AI?

Agentic AI is becoming popular because it helps businesses: 

• Save time: Automates repetitive tasks
• Make better decisions: Analyzes complex information quickly
• Boost productivity: Works around the clock
• Improve customer service: Solves problems faster

It’s like having a smart assistant who never sleeps, always works, and keeps getting better. 

In the future, Agentic AI might: 

• Watch over business metrics and fix issues before people notice
• Help teams collaborate by sharing important insights automatically
• Act as digital workers in every department

How is Agentic AI Built?

Agentic AI combines several powerful tools: 

• Large Language Models (LLMs): To understand and respond in human language
• RAG (Retrieval-Augmented Generation): To find the right information from documents
• Planning Engine: To decide what to do next
• Tools and APIs: To perform actions like sending alerts or updating systems
• Memory: To remember past interactions

Together, these tools help the AI think, plan, and act. 

How Agentic AI Takes Actions

While many AI tools are designed to provide answers or generate content based on prompts, Agentic AI goes a step further. It takes initiative and executes tasks autonomously. 

Instead of simply responding to queries, Agentic AI can observe, decide, and act based on real-time data and predefined goals. It’s not just a passive assistant; it’s an active participant in your workflows.

For example, Agentic AI can: 

• Send alerts if something unusual is found
• Turn on or off devices in an IoT system
• Fill out forms or update spreadsheets
• Trigger workflows like ordering parts or assigning tickets

It’s like hiring a digital team member who understands what to do and goes ahead and does it. 

How is Agentic AI Different from Gen AI?

Most people have used Gen AI tools like ChatGPT. These tools can answer questions, summarize content, or write emails. But they only do what you ask. 

Agentic AI goes further: 

With Gen AI:
You: “Summarize this file.”
Tool: Gives you a summary. 

With Agentic AI:
You: “Every Friday, check all reports, summarize them, and send them to my manager.”
Tool: 

• Looks for new reports every week
• Summarizes them
• Flags important issues
• Sends an email to your manager

Agentic AI understands your goal, plans the steps, and does the work. 

Bridgera’s Agentic AI: Meet JERA

At Bridgera, we built JERA to help teams make sense of their growing data. 

Businesses have manuals, help guides, spreadsheets, reports, and more. People waste time trying to find answers. 

JERA makes it easy.

Using RAG, JERA searches through thousands of documents and structured data. It then uses a language model to give clear, accurate answers. 

When someone asks JERA a question: 

• It understands the question using an LLM
• It searches its indexed knowledge base for the best answers
• It writes a helpful reply using only the correct information
• It presents the answer in a user-friendly chat or interface

But JERA can also: 

• Trigger actions in other systems
• Send notifications if a rule is broken
• Recommend solutions when something goes wrong
• Help with forms, checklists, and workflows

And JERA remembers. It adapts based on past conversations and becomes smarter over time. 

Conclusion: The Power of Agents

AI has evolved rapidly: 

• Machine learning enabled us to predict patterns and classify data
• Generative AI empowered us to create, communicate, and collaborate
• Agentic AI is now helping us take action, automate processes, and achieve more with less effort

Agentic AI represents the next frontier. By combining knowledge, reasoning, memory, and the ability to act, it transforms passive systems into proactive digital teammates. Tools like JERA are at the forefront of this transformation. 

At Bridgera, we believe intelligent agents will soon be indispensable across industries. They’ll handle repetitive tasks, accelerate decision-making, and unlock new levels of productivity—allowing people to focus on what truly matters. 

Because in the end, knowing is powerful—but acting on that knowledge is what drives real progress. 

About the Author
Joydeep Misra, SVP of Technology

Joydeep Misra is a technologist and innovation strategist passionate about turning complex data into simple, actionable intelligence. At Bridgera, he leads initiatives that blend IoT, AI, and real-world operations to help businesses move from connected to truly autonomous systems. With over a decade of experience in building enterprise-grade platforms, Joydeep is a strong advocate for practical AI adoption and believes that the future belongs to those who can make machines think and act.