What Is Agentic AI? The Simple Guide to Self-Driving Software

Quick Overview:

Agentic AI refers to "self-driving software" that handles an entire task from start to finish, operating as a "helper" rather than an assistant.:

• It accepts a goal, automatically breaks it into steps, and takes action without needing repeated prompts, unlike Traditional AI.
• The system operates independently by constantly tracking progress, evaluating the outcome of each step, and adjusting its plan if issues arise.
• Key capabilities include real-time decision-making, multi-step reasoning, and tool collaboration for tasks like research, coding, and workflow management.

You’ll explore each of these concepts in detail as you move through this guide. To build these skills even further, you can check out upGrad’s curated Agentic AI Courses for career development.

Meaning of Agentic AI

It works a bit like a skilled helper who doesn’t wait for instructions at every step. You tell it what you need, and it figures out the path, works through the tasks, and adjusts if something changes. It doesn’t pause after every move. It keeps going until the job is done.

Jensen Huang highlighted this well in the NVIDIA GTC 2025 keynote, describing agentic AI as an AI “that has agency. It can explain how to solve a problem, plan an action, and take action.”

Also Read: Generative AI vs Traditional AI: Which One Is Right for You?

Key Traits of Agentic AI

1. It acts with a clear goal

Agentic AI follows a defined target from start to finish. Traditional AI waits for a prompt and responds only to that single request.

2. It forms its own steps

Agentic systems break a task into smaller actions on their own. Traditional AI depends on the user to guide each step.

3. It moves forward without repeated input

Once the plan is set, an agentic ai system continues working. Traditional AI stops after giving one answer.

4. It tracks progress and adjusts

Agentic systems review what they’ve done and change their next move if needed. Traditional AI does not correct its path unless the user asks again.

Simple comparison

Feature	Traditional AI	Agentic AI
Task flow	One step at a time	Multi-step flow
Control	User-driven	Goal-driven
Adaptation	Limited	Adjusts as it works
Output	Immediate reply	Completed task

Also Read: Types of AI: From Narrow to Super Intelligence with Examples

Working Process of Agentic AI

This section helps you understand what happens behind the scenes and why an AI agent can handle long tasks with steady progress.

Step 1: Read and understand the goal

The system begins by taking in the goal you set. It checks the context, looks at the available data, and forms a clear idea of what the final output should be.

Step 2: Plan the steps

It breaks the goal into smaller actions. These steps create a simple path the agent can follow from the starting point to the final result.

Step 3: Take action

The ai agent starts working through the plan. It performs each action in order, such as gathering information, creating content, or running calculations.

Step 4: Check the outcome

After completing a step, the system reviews the output. It checks if the result is useful and if it matches the direction of the goal.

Step 5: Adjust when needed

If something doesn’t look right, the system updates the plan. It corrects mistakes, tries a different approach, or refines the step to stay on track.

Step 6: Continue until completion

The system repeats the cycle of acting, checking, and adjusting. It keeps going until the entire task is completed and the goal is met.

Also Read: How to Learn Artificial Intelligence: A Step-by-Step Roadmap

Simple Overview Table

Stage	What happens
Goal intake	The system reads the target
Planning	It breaks the task into steps
Action	It completes each step
Evaluation	It checks what worked
Adjustment	It fixes any issue

Also Read: AI Automation Explained: Tools, Benefits, and How It Differs From Automation

Main Capabilities of Agentic AI

Below are the core abilities that help agentic AI operate with independence, make decisions on the fly, and handle tasks that usually need human judgment.

1. Understands goals with context
   Agentic AI doesn’t just read the target. It understands the surrounding details, past actions, constraints, and user intent. This helps it make choices that fit the bigger picture, not just the immediate request.
2. Makes decisions in real time
   Instead of waiting for full instructions, the system chooses what to do next based on the situation. It can compare options, pick the best step, and shift direction when required.
3. Handles multi-step reasoning
   Agentic systems can follow logic chains, connect ideas, and work through complex reasoning tasks that need more than a single answer. This makes them suitable for research, coding, and planning work.
4. Works with memory and awareness
   The system uses short-term and long-term memory to stay aware of what has happened so far. This awareness helps it maintain consistency and avoid breaking the flow.
5. Collaborates with tools and environments
   An AI agent can interact with external tools, apps, or data sources. It can gather information, run actions, update files, or trigger workflows in the background.
6. Learns from feedback and outcomes
   Agentic AI improves after each run. It picks up patterns, notices mistakes, and updates how it works. This makes the system more reliable over time.

Also Read: No Code AI: A Beginner’s Guide to Building AI Without Writing Code

Types of AI Agents

In classic AI theory (like in Russell & Norvig), agents are grouped into five categories:

1. Simple Reflex Agents
These agents respond to the current situation only. They don’t store past actions or remember earlier steps. They work well for simple tasks where each decision depends on what is happening right now.

2. Model-based Agents
These agents use a basic internal picture of the world. They can track changes and make better decisions because they understand how one step affects the next.

3. Goal-based Agents
These agents act with a clear target in mind. They evaluate each action based on how close it moves the system toward the goal. Agentic ai builds heavily on this idea.

4. Utility-based Agents
These agents look for the best possible outcome. They compare different choices and pick the one with the highest value or benefit.

5. Learning Agents
These agents improve over time. They learn from actions, feedback, and mistakes. This helps them handle tasks that change or grow more complex.

Also Read: How to Build Your Own AI System: Step-by-Step Guide

Simple Comparison

Type of agent	Key idea	When it helps
Simple Reflex	Acts on the current state	Simple, instant decisions
Model-based	Uses a basic internal picture	Tasks needing awareness
Goal-based	Moves toward a target	Long or structured tasks
Utility-based	Picks the best action	Choices with trade-offs
Learning	Improves with experience	Tasks that evolve

These types form the foundation for modern ai agent design. Agentic systems combine several of these traits to plan, act, and adjust while working through full workflows.

Also Read: AI in Data Science: What It Is, How It Works, and Why It Matters Today

Real Examples of AI Agents

Below are some examples of AI agents we see in everyday tools and workflows.

• Customer support: Agents manage full conversations, resolve issues, and guide users without needing repeated prompts.
• Research tasks: Agents scan sources, gather facts, and turn information into clear summaries.
• Coding work: Agents write code blocks, fix errors, and run tests to complete development tasks.
• Workflow management: Agents create reports, update files, schedule tasks, and handle routine operations.
• Personal assistance: Agents set reminders, organise notes, and complete daily digital tasks to support users.

Also Read: Comprehensive Artificial Intelligence Syllabus to Build a Rewarding Career

Become an Expert in Agentic AI with upGrad!

Agentic AI is transforming industries by enabling systems to make decisions and perform tasks autonomously, without human intervention. However, many professionals face challenges in mastering this complex technology, from understanding its core principles to implementing it in practical scenarios. 

With practical insights and hands-on projects, upGrad’s courses equip you to build AI solutions for industries like healthcare, finance, and autonomous vehicles. 

Here are some free courses to help you start your journey in the AI sector.

• Fundamentals of Deep Learning and Neural Networks
• Introduction to Generative AI
• Learn Python Libraries: NumPy, Matplotlib & Pandas
• ChatGPT for Developers
• Analyzing Patterns in Data and Storytelling

Struggling to utilize the full potential of Agentic AI? upGrad offers personalized career counseling to help you choose the best path as per your goals. You can also visit your nearest upGrad center to gain hands-on experience through expert-led courses and real-world projects.