Introduction into AI Agents

Key Concept

An AI Agent is a system that leverages an AI model to interact with its environment to achieve user-defined objectives through reasoning, planning, and executing actions.

Core Components of an AI Agent

An AI Agent consists of two main parts:

1. The Brain (AI Model)

The BrainLarge Language Model

• Houses all cognitive processes
• Handles reasoning and planning
• Makes decisions about which actions to take
• Processes and analyzes situations

2. The Body (Capabilities and Tools)

• Represents the agent’s action capabilities
• Executes planned actions
• Interacts with the environment
• Limited by available tools and permissions

The Spectrum of Agency

AI Agents exist on a continuous spectrum of increasing autonomy and capability:

Agency Level	Description	Classification	Implementation Pattern
☆☆☆	No program flow impact	Simple processor	process_llm_output(llm_response)
★☆☆	Basic control flow decisions	Router	if llm_decision(): path_a() else: path_b()
★★☆	Function execution control	Tool caller	run_function(llm_chosen_tool, llm_chosen_args)
★★★	Program continuation control	Multi-step Agent	while llm_should_continue(): execute_next_step()
★★★	Cross-agent workflow control	Multi-Agent	if llm_trigger(): execute_agent()

AI Models in Agents

Large Language Models (LLMs)

The most common AI models used in agents are LLMs, which:

• Process text input and generate text output
• Include models like GPT-4, LLama, and Gemini
• Provide strong generalization capabilities

Other Model Types

• Vision Language Models (VLMs)
• Multimodal models
• Specialized domain models

Taking Action: Tools and Capabilities

While LLMs can only generate text, agents become powerful through their tools:

def send_message_to(recipient, message):
    """Useful to send an e-mail message to a recipient"""
    ...

Tools enable agents to:

• Interact with external systems
• Modify their environment
• Complete real-world tasks
• Process and generate various data types

Real-World Applications

1. Personal Virtual Assistants

• Digital assistants like Siri and Alexa
• Natural language interaction
• Environment control and task execution
• Information retrieval and processing

2. Customer Service Chatbots

• Natural language customer support
• Issue resolution and tracking
• Transaction processing
• Adaptive response generation

3. AI NPCs in Gaming

• Dynamic character behavior
• Contextual responses
• Adaptive gameplay interactions
• Enhanced player experience

Key Capabilities

An effective AI Agent can:

1. Understand Natural Language

   • Interpret human instructions
   • Process context and nuance
   • Generate meaningful responses

2. Reason and Plan

   • Analyze situations
   • Make informed decisions
   • Develop action strategies

3. Interact with Environment

   • Gather information
   • Execute actions
   • Monitor outcomes
   • Adapt based on results

Coming Up Next

In the following sections, we’ll dive deeper into LLMs - the “brain” that powers AI Agents - and explore how they enable sophisticated reasoning and decision-making capabilities.

Related Content

For a more comprehensive exploration of AI Agents, check out these detailed pages:

• Detailed Agent Introduction
• Multi-Agent Systems
• Agent Tools & Capabilities
• Agent Frameworks
• Implementation Approaches
• Agent Monitoring

If you’re interested in challenges when implementing AI agents, see:

• Tool Use & Augmentation
• Planning & Reasoning
• Memory Management
• Security & Safety