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Agent Architecture

Understanding how AI agents are structured will help you build more effective agents. This lesson covers the fundamental components of an agent’s architecture.

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Basic Components of an AI Agent

Every AI agent, regardless of complexity, has these basic components:

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1. Input (Sensors)

This is how agents receive information from their environment.

Examples of Input

  • Text input from users
  • Data from databases
  • Image or audio input
  • API responses
  • Sensor readings (in physical agents)

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2. Processing Unit

This component processes information and converts it into a format the agent can understand.

Processing Functions

  • Data cleaning and transformation
  • Feature extraction
  • Context building
  • Information retrieval
  • Pattern recognition

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3. Decision-Making Core

The “brain” of the agent that determines what actions to take.

Decision Components

  • Language models (like GPT-4)
  • Rule systems
  • Planning algorithms
  • Knowledge base
  • Memory systems

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4. Output (Actions)

The actions the agent can perform to achieve its goals.

Action Examples

  • Generating text responses
  • Creating visual content
  • Making API calls
  • Controlling other systems
  • Updating databases

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The Agent Loop

Agents operate in a continuous loop: This cycle allows agents to continuously:
  1. Gather information
  2. Update their understanding
  3. Make new decisions
  4. Take appropriate actions

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PraisonAI Agent Architecture

In the PraisonAI framework, agents follow a specific architecture:

PraisonAI Agent Components

  • Instructions: Defines the agent’s purpose and behavior
  • Language Model: Powers the agent’s intelligence (e.g., GPT-4)
  • Memory: Stores context and previous interactions
  • Tools: Specialized capabilities an agent can use

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Simple Agent Structure

from praisonaiagents import Agent

# Create a simple agent
research_agent = Agent(
    instructions="Research the latest developments in renewable energy",
    name="ResearchAgent"
)

# Start the agent
research_agent.start()

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Understanding Agent Communication

Multi-agent systems allow agents to communicate with each other: Each agent can:
  • Pass information to other agents
  • Request assistance from specialized agents
  • Collaborate on complex tasks

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Key Takeaways

Component Importance

Each component plays a vital role in the agent’s functionality

Agent Customization

You can customize each component based on your specific needs

Component Balance

A well-designed agent balances all components effectively

Continuous Improvement

Agents can be improved by enhancing individual components
In the next lesson, we’ll explore how to define effective instructions for your AI agents.