What Is Agentic AI and How Is It Different from Chatbots?

Agentic AI can plan and take actions to complete a goal. Chatbots mainly respond to questions.

What Is Agentic AI? (Explained Simply)

Agentic AI is when you tell an AI what you want done, not how to do it step by step.

Instead of waiting for instructions at every stage, the AI figures out the next steps on its own. It works through the task, takes actions when needed, and stops if it needs help from a person.

You can think of it like giving a task to a helpful assistant. You explain the goal, and the assistant handles the details.

For example, instead of asking AI to write one email, agentic AI can:

Decide what information is needed
Pull data from tools or systems
Draft, review, and update the output
Take the next action automatically

This is what makes agentic AI useful in real applications, not just demos.

What Are Chatbots Designed to Do?

Chatbots are mainly built to reply to what you ask. You type a question, and they give you an answer.

They’re useful for conversations, quick support, or handling simple requests. Once they respond, their part is usually done. If something needs multiple steps, the user has to keep guiding the conversation.

Chatbots don’t normally think ahead or decide the next action by themselves. They wait for instructions and respond one step at a time.

The Core Difference: Responding vs Acting

The easiest way to understand the difference is this:

Chatbots respond to prompts
Agentic AI works toward a goal

Agentic systems can remember context, decide next steps, use tools or APIs, and continue working with minimal human input.

Agentic AI vs Chatbots – Key Differences

Level of Autonomy: Chatbots need constant prompts. Agentic AI works with limited supervision.
Task Complexity: Chatbots handle single questions. Agentic AI handles multi-step tasks.
Usage: Chatbots mainly generate text. Agentic AI can call tools, APIs, and systems.
Decision-Making: Chatbots respond. Agentic AI decides and acts.

Why Agentic AI Is Becoming Important

As AI starts showing up in everyday products and work tools, people are realizing something pretty quickly. Chatting with AI is useful, but it’s not enough. Teams need AI that can actually do things, not just talk about them.

That’s where agentic AI comes in. Instead of waiting for instructions at every step, these systems can follow a process, use tools, and move tasks forward on their own. This makes AI far more practical for real work, especially when teams don’t have time to babysit every action.

Learning agentic AI helps you keep up with how AI is being used right now. More importantly, it puts you ahead of where things are going next, as AI becomes a regular part of how work gets done.

How This Certification Helps You Learn Agentic AI

The NVIDIA-Certified Professional: Agentic AI (NCP-AAI) certification teaches you how agentic AI systems work from the ground up.

You learn how agents plan tasks, make decisions, use tools, work together, and operate safely with human oversight. This helps you move from understanding AI concepts to building systems that actually perform work in real environments.

Why Get the NVIDIA-Certified Professional: Agentic AI Certification?

Validates your ability to build and operate autonomous AI systems responsibly in real environments
Prepares you for the shift from prompt-based AI to agentic AI that can plan, reason, and take action
Focuses on production-ready skills, not just experimentation or demos
Builds confidence in deploying, scaling, and monitoring agentic AI systems
Strengthens your understanding of governance, safety, and reliability in autonomous AI
Demonstrates your ability to design human-in-the-loop controls for trust and compliance
Aligns your skills with what enterprises expect from advanced AI professionals

Who Should Take This Certification?

This certification is ideal for professionals already working with advanced AI systems.

This certification is a strong fit for:

AI Engineers and AI Developers
Solutions Architects working on AI platforms
AI Strategists and Technical Leads
Senior Machine Learning Engineers
Professionals designing multi-agent or orchestration-based systems

If you already work with AI models and want to move into autonomous, decision-driven AI, this certification is a natural next step.

Recommended Experience for the NCP-AAI Exam

Best suited for learners with 1–2 years of experience in AI or machine learning roles
Ideal if you’ve worked with real, production-level AI systems, not just demos
Helpful to have experience deploying models and running them in live environments
Familiarity with integrating tools or APIs into AI workflows is a plus
Basic exposure to reasoning workflows or agent-style systems is beneficial
Understanding of monitoring, logging, and system reliability helps you learn faster
Awareness of AI safety, governance, or ethical practices is useful but not mandatory

What Skills Do You Gain?

Build intelligent AI agents that can plan tasks, make decisions, and take action
Design both single-agent and multi-agent systems with clear roles and coordination
Define how agents communicate, share information, and work together
Integrate tools, APIs, and external services to extend agent capabilities
Combine language models with other AI systems for complex workflows
Deploy agentic AI systems in real production environments
Monitor agent behavior and performance over time
Apply safety, ethics, and control mechanisms to keep AI systems reliable

Skills Assessed in the NCP-AAI Exam

The exam tests real-world agentic AI skills used in enterprise systems.

The exam covers the following domains:

Agent Architecture and Design
Agent Development and Orchestration
Cognition, Planning, and Memory
Knowledge Integration and Data Handling
Evaluation and Performance Tuning
Deployment and Scaling
NVIDIA Platform Implementation
Run, Monitor, and Maintain
Safety, Ethics, and Compliance
Human-AI Interaction and Oversight

These areas reflect how agentic AI systems are actually built and operated in production.

NCP-AAI Exam Details, Format, and Validity

A clear exam structure helps you prepare effectively.

NVIDIA-Certified Professional: Agentic AI (NCP-AAI)
Prior Certification	Not Required
Exam Validity	2 years
Exam Fee	$200 USD
Exam Duration	90-120 minutes
No. of questions	60-70 Questions
Passing Marks/Score	70%
Recommended Experience	1–2 years of experience in AI/ML roles, focusing on production-level agent development, strong knowledge of multi-agent frameworks, reasoning, and orchestration.
Exam Format(Question Types)	Multiple Choice, Multiple Response, Scenario-based
Languages	English