---
title: "AI Agents"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# AI Agents

[Introduction to LLM Agents](https://developer.nvidia.com/blog/introduction-to-llm-agents/)

[Which AI Agent framework should i use?](https://medium.com/@aydinKerem/which-ai-agent-framework-i-should-use-crewai-langgraph-majestic-one-and-pure-code-e16a6e4d9252)

[Why Vertical LLM Agents Are The New $1 Billion SaaS Opportunities](https://www.youtube.com/watch?v=eBVi_sLaYsc)

An Artificial Intelligence (AI) agent is a software program designed to autonomously perceive its environment, make decisions, and execute actions to achieve specific goals. These agents operate without direct human intervention, utilizing sensors to gather information and actuators to interact with their surroundings. ￼

Key Characteristics of AI Agents:
	1.	Autonomy: AI agents function independently, making decisions and performing tasks without human oversight. ￼
	2.	Perception: They collect data from their environment through various input methods, such as cameras, sensors, or data streams. ￼
	3.	Reasoning and Decision-Making: AI agents analyze the gathered information using algorithms and models to make informed decisions aimed at achieving their objectives. ￼
	4.	Learning: Many AI agents can learn from their experiences, enhancing their performance over time through techniques like machine learning and reinforcement learning. ￼
	5.	Action: Based on their reasoning, AI agents take appropriate actions to influence their environment and progress toward their goals. ￼

Types of AI Agents:

AI agents can be categorized based on their complexity and capabilities:
	1.	Simple Reflex Agents: These agents respond directly to current perceptions using predefined rules, without considering past experiences. ￼
	2.	Model-Based Reflex Agents: They maintain an internal model of the world, allowing them to handle partially observable environments by considering both current perceptions and historical data. ￼
	3.	Goal-Based Agents: These agents act to achieve specific goals, using planning and decision-making processes to determine the best actions. ￼
	4.	Utility-Based Agents: They evaluate different actions based on a utility function, aiming to maximize overall satisfaction or performance. ￼
	5.	Learning Agents: These agents improve their performance over time by learning from their experiences and adapting their behaviors accordingly. ￼

Applications of AI Agents:

AI agents are utilized across various domains, including:
	•	Virtual Assistants: Agents like Siri, Alexa, and Google Assistant interact with users, providing information and performing tasks based on voice commands.
	•	Autonomous Vehicles: Self-driving cars use AI agents to perceive their surroundings, make driving decisions, and navigate safely.
	•	Customer Service: Chatbots and virtual customer service agents handle inquiries, provide support, and resolve issues without human intervention.
	•	Finance: AI agents assist in fraud detection, trading, and personalized financial advice by analyzing vast amounts of data.
	•	Healthcare: Agents support diagnostics, patient monitoring, and personalized treatment plans by processing medical data and learning from patient outcomes.

The development and deployment of AI agents continue to evolve, offering significant potential to enhance efficiency and effectiveness across various industries.
