Agentic AI Explained

Artificial intelligence has evolved beyond chatbots and content generation—welcome to the era of agentic AI. 

Unlike traditional AI models, which respond to prompts and human input, agentic AI operates autonomously, using reasoning, adaptive learning, and decision-making to tackle complex challenges.

Discover what makes agentic AI unique, how it differs from generative AI, and real-world use cases for this evolving technology.

What is agentic AI?

Agentic AI is an advanced AI system that can operate independently without constant human input. Unlike AI chatbots that create outputs based on queries, an agentic AI system uses reasoning, iterative planning, and adaptive problem-solving to tackle complex problems. 

These systems analyze data from multiple sources, develop strategies, and take action to achieve specific goals. Agentic AI systems have the potential to enhance customer experiences, automate manual processes, and improve operational efficiency across industries.

With agentic AI, organizations can create software programs, called AI agents, to a specific task for an employee, team, or system. These autonomous agents can learn, adapt, and operate independently.

For example, instead of providing basic troubleshooting help like a chatbot, an AI tech support agent can diagnose and fix Wi-FI issues by running tests and checking device settings.

How is agentic AI different from generative AI?

Generative AI paved the way for more advanced AI technology. Since it has been around longer, more people are familiar with it and have used it for various tasks, from drafting text to creating images and videos.

Generative AI learns from patterns in input data to create new content. It can generate text, images, videos, and even code by predicting the next likely element. 

The biggest difference between generative AI and agentic AI is that generative AI requires human oversight to produce content, while autonomous AI agents can operate independently without human intervention.

Agentic AI goes beyond content generation and can analyze situations, make decisions, and take actions without direct human intervention, making it valuable for automation, problem-solving, and complex decision-making.

Gen AI vs. agentic AI

Agentic AI use cases

Agentic AI isn’t just another chatbot—it’s like an intelligent assistant that takes action to solve problems. Thanks to its ability to learn and adapt, it’s applicable across industries to solve real challenges. 

Whether detecting fraud, scheduling medical appointments, or speeding up government processes, this AI works behind the scenes to make life easier for businesses and customers. Here are some real-world examples.

Fraud detection and prevention for financial services

The challenge: Financial institutions often struggle to detect and prevent fraud in real time, leading to delays, financial losses, and frustrated customers. Traditional fraud detection methods rely on rule-based systems that generate false positives, which can lead to alert fatigue and cause real issues to go unaddressed. 

Financial organizations can create agentic AI workflows to:

• Continuously monitor transactions and detect anomalies. 
• Use adaptive learning to distinguish between legitimate and fraudulent activity
• Take action to resolve the problem, such as freezing a suspicious transaction without human intervention.
• Notify customers and provide automated resolutions, reducing delays and manual reviews.

Example: A bank’s AI agent detects and temporarily blocks an unusual high-value transaction from a new location. The agent then verifies the customer’s identity through an automated workflow before and based on the results, approves or declines the transaction.

Patient scheduling for healthcare organizations

The problem: Hospitals and health clinics struggle with scheduling conflicts, patient cancellations, and no-shows.

Hospitals can use agentic AI to:

• Automate patient scheduling by analyzing doctor availability, patient history, and urgency.
• Predict potential cancellations and proactively reschedule patients based on patterns.
• Send automated reminders to reduce no-shows.

Example: A hospital’s AI agent identifies a likely cancellation based on past patient behavior. The system proactively reaches out with an earlier time slot and assigns a new patient to the open time slot.

Permit processing and approvals for public organizations

Problem: The process of obtaining business or construction permits is time-consuming due to processing, manual reviews, and lots of paperwork. This is frustrating for businesses and citizens.

Public organizations can use agentic AI systems to:

• Automate permit application review by analyzing submitted documents and referencing regulatory requirements.
• Flag missing or incorrect information and guide applicants to correct errors.
• Approve simple applications while escalating more complex cases to human officials.

Example: A city’s AI agent reviews a small business permit application, verifies compliance, and grants approval almost instantly, reducing the standard processing time.

Improving incident response with agentic AI

These are just a few examples of how agentic AI and AIOps can improve how teams detect, analyze, and resolve incidents, helping organizations reduce response times and human error. These technologies proactively identify anomalies, take action to resolve issues, and continuously learn from past incidents, helping to make security and IT teams more efficient and resilient.

Integrating AIOps and agentic AI is a competitive advantage for businesses looking to improve their incident response strategy. Discover how PagerDuty’s AIOps can help you reduce incident response time, automate processes, and streamline operations. Start your free trial today.