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🧩 Welcome to DataPro #157

Your weekly pulse on data, AI, and the people shaping its future.

This week we feature Sagar Lad, a Data & AI Solution Architect who’s passionate about making complex AI ideas approachable. In his latest article, “What Are AI Agents? A Simple Guide,” Sagar unpacks one of the most fascinating developments in AI, systems that can perceive, reason, and act autonomously.

From self-driving cars that interpret their surroundings to chatbots that learn from conversations, Sagar explains how AI agents work under the hood, detailing how they perceive input, make decisions, and evolve through feedback. It’s a crisp, example-driven read that helps you grasp how machine autonomy is reshaping everything from robotics to analytics.

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What Are AI Agents? A Simple Guide by Sagar Lad

Artificial Intelligence (AI) is transforming the way we live and work. One of the most exciting developments in this field is the rise ofAI agents. But what exactly are they? Let’s break it down in a way that’s easy to understand.

What Is an AI Agent?

AnAI agentis a computer program that canperceive its environment,make decisions, andtake actionsto achieve a goal. Think of it like a digital assistant that doesn’t just follow instructions — it learns, adapts, and acts intelligently.

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Task Execution Flow by AI Agents

Example:

Imagine a robot vacuum cleaner. It senses where dirt is, avoids obstacles, and decides the best path to clean your room. That’s an AI agent in action!

Internal Working of an AI Agent

1. Perception (Input Layer)

The agent receives data from its environment.
This could be sensor data (for robots), user input (for chatbots), or digital signals (for trading bots).
Internally, this data is processed into a format the agent can understand often as structured data or features.

Example:A self-driving car uses cameras and sensors to detect pedestrians, traffic lights, and road signs.

2. Knowledge Base / State Representation

The agent maintains a model of the world or its environment.
This could be a simple memory of past actions or a complex map of the surroundings.
It helps the agent understand context and make informed decisions.

Example:A chess-playing agent remembers the current board state and possible moves.

3. Decision-Making (Reasoning Engine)

Based on the perceived data and internal state, the agent decides what to do next.

This can involve:

Rule-based logic(if-then rules)
Search algorithms(finding the best path or solution)
Machine learning models(predicting outcomes)
Reinforcement learning(learning from trial and error)

Example:A recommendation agent uses a machine learning model to suggest movies based on your viewing history.

4. Action (Output Layer)

The agent performs an action in the environment.
This could be sending a message, moving a robot arm, or updating a database.

Example:A chatbot replies to a user query with a helpful answer.

5. Learning

Some agents improve over time by learning from feedback or new data.
Supervised learning(learning from labeled data)
Unsupervised learning(finding patterns)
Reinforcement learning(learning from rewards and penalties)

Example:A game-playing agent gets better by playing thousands of games and learning which strategies win.

🔄 The Agent Loop

Here’s a simplified loop that most AI agents follow:

Perceive → Analyze → Decide → Act → Learn (optional) → Repeat

👉 Continue reading the full article on the Packt Medium handle.

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See you next time!