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/english > 39. AI Agent Architecture
// УРОК 39

AI Agent Architecture

B2

AI Agent Architecture

An AI agent is an LLM that can reason, plan, use tools, and take actions to complete a goal.

Core Components

ComponentRole
LLM (brain)Reasoning and decision making
toolsFunctions the agent can call (search, code execution, APIs)
memoryShort-term (context window) + long-term (vector store)
plannerBreaks down complex goals into steps
executorRuns the planned steps and handles tool outputs

Agent Patterns

  • ReAct: Reason + Act — the model alternates between thinking and taking actions
  • Plan-and-Execute: First create a full plan, then execute each step
  • Multi-agent: Multiple specialized agents coordinated by an orchestrator

Useful Phrases

  • "The agent uses a ReAct loop — it reasons about the next action, executes it, then observes the result."
  • "We have a orchestrator agent that delegates subtasks to specialized worker agents."
  • "Long-term memory is stored in a vector database and retrieved when relevant."
// TERMINAL CHALLENGE

Проверь себя

Q1. What is the key difference between an AI agent and a simple LLM call?
Q2. What is the ReAct pattern?
Q3. In a multi-agent system, what does the orchestrator do?
Q4. What is the role of 'memory' in an AI agent?
Q5. Complete: 'The agent ___ a plan, then ___ each step while handling tool outputs.'
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