AI agents can perform tasks, interact with tools, and generate responses based on user instructions. However, agents need access to relevant previous information to maintain context across multiple interactions.

Memory systems help AI agents store, retrieve, and manage information during and across tasks. They are useful for building personalized assistants …

Introduction

Large Language Models (LLMs) can answer questions, generate code, summarize information, and explain complex topics.

But answering a question is different from completing a task.

For example, an LLM can explain how to check an order status, but an AI agent with access to an order system can actually …

Introduction

AI agents are different from traditional chatbots because they can do more than generate text. An agent can interact with external tools such as APIs, databases, search engines, calculators, code interpreters, file systems, and business applications.

But this creates an important question:

How does an AI agent decide which …

## Introduction

Chatbots and AI agents are both becoming common in modern applications, but they are not the same.

A chatbot is primarily designed to communicate with users and provide responses, while an AI agent is designed to understand a goal, make decisions, use tools, and take actions to accomplish that …

## Introduction

AI agents are moving beyond simply generating text. Modern AI agents can interact with external systems, retrieve information, call APIs, query databases, execute functions, send messages, and perform actions on behalf of users.

This capability is commonly enabled through tool calling or function calling.

For example, an AI travel …