Beginner

Introduction to AI Agents

AI agents are autonomous systems that can reason, plan, and take actions to accomplish goals. They represent the next evolution beyond simple chatbots — moving from answering questions to actually doing work.

What Are AI Agents?

An AI agent is a system that uses a Large Language Model (LLM) as its reasoning engine to autonomously decide what actions to take to accomplish a given goal. Unlike a chatbot that simply responds to messages, an agent can:

Reason about the current situation and what needs to happen next
Plan a sequence of steps to achieve a goal
Take actions by calling tools, APIs, or executing code
Observe results and adapt its approach based on what happened
Persist through multi-step tasks without human intervention at each step

💡

Key distinction: A chatbot waits for your next message. An agent takes initiative. You give it a goal, and it figures out the steps, executes them, handles errors, and delivers results.

Agent vs Chatbot vs Assistant

These terms are related but describe different levels of autonomy:

System	Autonomy	Actions	Example
Chatbot	None — responds to each message	Text generation only	Basic customer service bot
AI Assistant	Low — helps with tasks when asked	Can use some tools (search, code)	ChatGPT, Claude on claude.ai
AI Agent	High — pursues goals autonomously	Multiple tools, multi-step workflows	Claude Code, Codex CLI, Devin

The ReAct Paradigm

ReAct (Reasoning + Acting) is the foundational framework for how modern AI agents work. Published in a 2022 paper by Yao et al., ReAct interleaves reasoning traces with actions in a loop:

Thought
The agent reasons about the current state and decides what to do next. "I need to find the user's order status. I should query the orders database."
Action
The agent executes a tool or takes an action. query_database(order_id="12345")
Observation
The agent observes the result of the action. "Order 12345: shipped on March 10, tracking number XYZ123."
Repeat
The agent reasons about the observation and decides whether to take another action or provide a final answer.

ReAct Loop (Pseudocode)

while not task_complete:
    # 1. Think: LLM reasons about the situation
    thought = llm.think(task, observations)

    # 2. Act: LLM decides and executes an action
    action = llm.decide_action(thought)
    result = execute_tool(action)

    # 3. Observe: Add result to context
    observations.append(result)

    # 4. Check: Is the task complete?
    task_complete = llm.is_done(observations)

Real-World Agent Examples

Coding Agents

Claude Code: Anthropic's agentic coding tool. Reads your codebase, writes and edits files, runs commands, manages git, and handles multi-step development tasks autonomously.
OpenAI Codex (CLI): OpenAI's coding agent that can understand codebases, write code, run tests, and fix bugs through a terminal interface.
Google Jules: Google's AI coding agent integrated with GitHub that can pick up issues, write code, and submit pull requests.
GitHub Copilot Agent: An evolution of Copilot that can autonomously work on issues, create branches, and submit PRs.

Research Agents

Deep Research (multiple providers): Agents that can browse the web, read papers, synthesize information, and produce comprehensive research reports.
Perplexity: A search agent that queries multiple sources, synthesizes findings, and provides cited answers.

Browsing Agents

Computer Use agents: Agents that control a computer's mouse, keyboard, and screen to interact with any application or website.
Web browsing agents: Agents that can navigate websites, fill forms, extract data, and complete web-based tasks.

Business Agents

Customer support agents: Handle customer inquiries end-to-end, including looking up orders, processing refunds, and escalating to humans.
Data analysis agents: Receive a question about data, write SQL queries, create visualizations, and present insights.

Why Agents Matter Now

AI agents have become practical because of three converging advances:

LLM capability: Modern LLMs (Claude Opus 4, GPT-4o, Gemini 2.5 Pro) are smart enough to reliably reason about complex tasks and make good decisions about what actions to take.
Tool use / function calling: All major LLM providers now support structured function calling, allowing agents to interact with external tools, APIs, and systems reliably.
Long context windows: With 200K-2M token contexts, agents can hold an entire project, conversation history, and tool results in memory simultaneously.

✅

The future is agentic: Industry consensus is moving toward agents as the primary way humans interact with AI. Rather than crafting perfect prompts for each step, you describe the goal and the agent handles the execution.

What You'll Learn in This Course

This course takes you from understanding what agents are to building and deploying them in production:

How agent architecture works (LLM + tools + memory + planning)
Choosing and using agent frameworks (LangGraph, CrewAI, AutoGen)
Implementing tool use with function calling and MCP
Building memory systems for agents
Creating multi-agent systems for complex tasks
Building a complete agent from scratch
Production best practices for reliability, safety, and cost

Next → Agent Architecture