What is an AI Agent?

Overview

An AI Agent is an autonomous system that utilizes Large Language Models (LLMs) to understand, reason, plan, and execute tasks with minimal human intervention. Unlike traditional AI models that respond passively to prompts, agents actively break down complex problems into manageable steps, leveraging memory, external tools, and adaptive behavior to achieve their goals.

At its core, an AI agent follows a structured process:

  1. Receive a Task → Takes user input (e.g., a command or question).

  2. Plan a Solution → Decomposes the problem, selects tools, and reasons through possible solutions.

  3. Execute the Plan → Retrieves information, performs computations, or interacts with external resources.

  4. Deliver Results → Presents a structured response, action, or decision.

AI Agents Diagram

Key Components of an AI Agent

An AI agent is composed of several interconnected modules, each responsible for specific functions that enable decision-making and execution.

  • Profiling Module (Agent Core) 🏗️

    Defines the agent’s **role, goals, and behavior** (e.g., financial analyst, teacher, assistant). This module selects tools, orchestrates task execution, and determines how the agent interacts with users.

  • Memory Module 🧠

    Stores past interactions and contextual data:
- **Short-term memory**: Tracks current session details for immediate recall.
- **Long-term memory**: Retains historical information to improve continuity across interactions.

  • Tools Module 🔧

    Connects the agent to **external resources** such as APIs, databases, or third-party services. This enables real-time data retrieval, calculations, and interaction with other systems.

  • Planning Module 📌

    Breaks down complex tasks into **smaller, manageable steps** to ensure structured decision-making and execution. This step-by-step reasoning improves efficiency and precision.
AI Agents Diagram

Anatomy of an AI Agent

An AI agent’s functionality is driven by the following key elements:

  1. Persona → Defines the agent’s identity and role.

  2. Instruction → Guides the agent on how to operate.

  3. Task & Planning → Helps the agent break down and structure its workflow.

  4. Memory → Allows it to recall and learn from past interactions.

  5. Tools → Expands its capabilities by interacting with external systems.

  6. Delegation → Enables collaboration with other agents or processes.

Together, these components empower AI agents to reason, strategize, and autonomously complete tasks within a defined context.

Agentic Integrations