AI Lesson 5 – intelligent Agents | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 5: Intelligent Agents

Artificial Intelligence systems do not just analyze data; they also act. An intelligent agent is the component of AI that observes its environment, decides what to do, and performs actions to achieve a goal. Understanding agents is critical because almost every real AI system — from Google Maps to self-driving cars — is built around the idea of an intelligent agent.

What Is an Intelligent Agent?

An intelligent agent is any entity that:

  • Perceives its environment using sensors
  • Makes decisions based on those perceptions
  • Acts upon the environment using actuators

The core idea is simple: perceive → decide → act. The intelligence of an agent depends on how well it chooses actions to achieve its goals.

Real-World Examples of Intelligent Agents

You already interact with intelligent agents every day:

  • Google Maps: observes traffic data and suggests optimal routes
  • Email spam filters: analyze messages and decide whether to block them
  • Netflix recommendations: observe viewing behavior and suggest content
  • Self-driving cars: perceive roads, pedestrians, and signals, then decide how to drive

In all these cases, the system continuously observes, evaluates, and acts.

Agent and Environment

An intelligent agent always operates inside an environment.

  • The agent is the decision-maker
  • The environment is everything the agent interacts with

For example, in a chess game:

  • The agent is the chess-playing AI
  • The environment is the chessboard, pieces, and opponent moves

How Does an Agent Decide What to Do?

At a basic level, an agent follows a policy — a rule or strategy that tells it which action to take in a given situation.

Early AI agents used fixed rules. Modern agents learn these policies from data using machine learning and reinforcement learning.

Simple Agent Logic (Conceptual)

Let’s look at a very simple agent idea:

  • If the room is dark → turn on the light
  • If the room is bright → do nothing

This is a basic rule-based agent. Real agents use more complex logic, but the foundation is the same.

Implementing a Simple Intelligent Agent (Python)

Below is a simple Python example that simulates an intelligent agent deciding an action based on its environment. 


def intelligent_agent(light_level):
    if light_level < 50:
        return "Turn ON the light"
    else:
        return "No action needed"

# Simulated environment input
print(intelligent_agent(30))
print(intelligent_agent(80))
Turn ON the light No action needed

Here, the agent:

  • Perceives the environment through light_level
  • Applies decision logic
  • Returns an action

Although simple, this structure is the foundation of more advanced AI agents.

Types of Intelligent Agents

AI agents are commonly classified into the following types:

  • Simple Reflex Agents: Act based on current perception only
  • Model-Based Agents: Maintain internal state
  • Goal-Based Agents: Act to achieve specific goals
  • Utility-Based Agents: Choose actions that maximize performance
  • Learning Agents: Improve performance over time

Modern AI systems usually combine multiple agent types into a single intelligent system.

Why Intelligent Agents Matter in AI

Without intelligent agents, AI would only analyze data without acting on it. Agents are what allow AI systems to interact with the real world, adapt to changes, and operate autonomously.

As you move forward into reinforcement learning, robotics, and LLM-based agents, this concept will appear again and again.

What’s Coming Next

In the next lesson, we will explore Informed and Uninformed Search — the techniques intelligent agents use to explore possibilities and choose optimal actions efficiently.

← Previous Course Index Next →